JP2023520579A - Systems and methods of dynamic micro-optical coherence tomography for mapping cellular function - Google Patents

Abstract

An apparatus for obtaining image data and functional data from a biological sample, the apparatus being configured to obtain interferometric information based on radiation provided by interference of the biological sample with a reference along an imaging plane at a plurality of time points. an interferometer, wherein at least one axis of the imaging plane is an axis along at least a depth dimension or an axial dimension; and a processor configured to receive interference information from the interferometer. , wherein the processor processes the interferometric information to generate an image of the biological sample along the imaging plane, and based on the multiple time points of the interferometric information, the temporal modulation induced by the mechanical features of the biological sample. An apparatus configured to determine reflected frequency information and generate a spatial map of frequency information indicative of mechanical function of a biological sample. [Selection drawing] Fig. 1

Description

<Cross reference to related applications>
No. 63/005,676 (filed April 6, 2020), 63/126,454 (filed December 16, 2020) 157,432 (filed March 5, 2021) and claiming priority to these applications, the entire disclosure of which is hereby incorporated by reference. shall be included in

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH
none.

Microscopic examination of tissue cells is central to understanding and diagnosing disease. Historically, the focus has been on identifying static cell phenotypes and morphological features. Such standard approaches sometimes miss important opportunities to determine cell phenotypes based on properties such as intracellular molecular movements that can reflect functions such as activation and proliferative states. be. Certain fluorescence microscopy techniques, phase-contrast microscopy, and single-cell (single-cell) interference microscopy are often used to investigate intracellular motility, but these microscopy techniques are readily available in tissues or patients. cannot be used for

Accordingly, example embodiments of the present disclosure relate to methods, systems, constructs and computer-accessible media for providing micro-optical coherence tomography procedures and, in particular, high-resolution images for mapping cellular function. Spatial and temporal resolution methods, systems, constructs and computer-accessible media.

Cross-sectional reflectance imaging techniques provide information arising from scattering from refractive index interfaces within tissue. Because this refractive index gradient is small and inconsistent, reflectance imaging often suffers from low contrast. Disclosed herein are systems and methods for imaging and analyzing the motion of particles within tissue at sub-cellular resolution to provide other imaging modalities. In addition, particle motion provides information about the mechanical properties of tissue that can be understood about function and metabolism.

Metabolism gives rise to molecular and organelle movements caused by intracellular processes, which can be different from thermally driven Brownian motion. Molecular motion caused by this process causes signal fluctuations in micro optical coherence tomography (micro-OCT) images. By recording this variation in the transverse imaging plane or in three dimensions and subsequently extracting the temporal frequency information, a frequency map can be obtained that can be superimposed on the morphological image. This technique is based on differences in frequency content between different organelle types and cell types, and is capable of delineating microscopic features of living tissue in a technique termed dynamic micro-OCT (d-micro-OCT).

Dynamic micro-OCT has been applied to human biopsies and chemotherapy-treated tumors grown in mouse models. Human biopsies reveal microscopic features (e.g. cell nuclei, organelles and cell membranes), cellular structures (e.g. squamous cells, glandular cells, inflammatory cells, etc.), and different cells not visible on the corresponding routine micro-OCT images. It is possible to use d-micro OCT to distinguish between species. In vitro experiments have also been performed with tumors grown by human cell lines. A custom-made microdevice containing small doses of chemotherapy drugs had been inserted into the tumor. For example, the effect of intratumoral apoptosis was differentiated by d-microOCT and confirmed by tissue imaging stained with cleaved caspase-3 (CC3) as an apoptosis marker.

Other reported approaches to study such tissue motion use non-OCT techniques (such as particle tracking in cell culture) or use OCT in the anphas (transverse) plane parallel to the tissue surface. Alternatively, a low-resolution type of OCT with sub-cellular resolution was used. Thus, d-micro-OCT is the only optical imaging technique that can provide sub-cellular resolution imaging in a transverse imaging plane that can probe the mechanical properties of individual cells throughout tissue.

Features of the above apparatus and procedures that may improve the results obtained include dynamic cross-sectional imaging for visualization of the metabolic activity of individual cells, algorithms for determining the frequency content of intracellular, cellular and extracellular motility. , and developing and using display techniques to visualize frequency content to distinguish distinct cellular components.

In certain embodiments, the benchtop d-micro OCT apparatus and methods disclosed herein are suitable for probe-based imaging systems capable of enabling clinical assessment of cytomechanical properties in human subjects. can do. One application is to incorporate different chemotherapy-loaded intratumoral microdevices on a patient-by-patient basis to assess the efficacy of different drugs. This reduces the time to determine the best treatment for an individual. Another application is that the d-micro OCT probe can be inserted into the human body to diagnose disease without ablating tissue. In yet another embodiment, d-micro OCT can be used externally to obtain microscopic images of skin.

In addition, the above additional information about cell function provides better diagnostic value than traditional biopsy or optical biopsy performed using conventional micro-OCT, and this additional information can be used in high morbidity risk subjects. It is also expected to be used as a surveillance tool.

In one embodiment, the present disclosure provides an apparatus for obtaining image and functional data from biological tissue, including in vivo tissue, excised tissue, three-dimensional cell cultures, spheroids, or organoids, and the like. The apparatus is an interferometer for obtaining interference information based on interference of radiation returned from living tissue with radiation provided by a reference along an imaging plane, the imaging plane extending through at least one tissue. an interferometer having a depth-resolved axis; and a processor configured to receive interference information from the interferometer, the processor processing the interference information to produce a morphological image of living tissue along an imaging plane. to derive frequency information from the temporal modulation of the interference information induced by tissue mechanical functions, and to generate reports spatially mapping the tissue mechanical functions, either alone or in conjunction with standard morphological images. is configured to

In another embodiment, the present disclosure provides a method for obtaining image data and functional data from living tissue, the method provided along an imaging plane by radiation returned from the living tissue and a reference. obtaining interferometric information based on interfering with radiation from the imaging plane, wherein the imaging plane has at least one tissue depth resolution axis, and processing the interferometric information to form a morphological image of the living tissue along the imaging plane. generating, determining frequency information from the temporal modulation of the interference information induced by the mechanical function of the tissue, and a report spatially mapping the mechanical function of the tissue and combining it with the morphological image. generating a report that can.

In another embodiment, the present disclosure provides systems and methods for improved spatial resolution of d-micro OCT. Improved spatial resolution is represented by using sub-pixel sampling methods, bulk sub-pixel motion, and optical aliasing. High spatial resolution allows visualization of more details within the cell. The present embodiments take advantage of tissue intrinsic motion that is at least partially independent of molecular/organelle motion (such as patient motion, tissue hydration, or mechanically induced subject motion). . By taking advantage of this motion, each image in the micro-OCT image sequence can be acquired from a different viewpoint, thereby achieving sub-pixel resolution while preserving d-micro-OCT information.

In another embodiment, the present disclosure provides systems and methods for improved temporal resolution of d-micro OCT. Temporal resolution is improved by Fourier transform spectral extrapolation. The higher the temporal resolution, the finer the frequency resolution in the low-frequency range of motion where the bulk of intracellular transport takes place. This advance allows for improved differentiation of cells and cell activity based on the frequency signature of intracellular movements.

In another embodiment, the present disclosure provides a system and method for single-scan d-micro OCT. Currently, d-micro OCT images take several seconds to acquire and are difficult to perform in vivo due to motion artifacts. Simultaneous phase acquisition with multiple spatially offset beams allows d-micro OCT to be performed in a single scan. This technique allows d-micro OCT to be performed in moving living organs using a handheld probe or a small diameter flexible probe inside the body.

In certain embodiments, the present disclosure provides an apparatus for obtaining image data and functional data from a biological sample, wherein the biological sample interferes with a reference along an imaging plane at multiple time points. wherein at least one axis of said imaging plane is an axis at least partially along a depth dimension or an axial dimension. and a processor configured to receive the interference information from the interferometer, the processor processing the interference information to generate an image of the biological sample along the imaging plane. determining frequency information reflecting temporal modulation caused by a mechanical function of the biological sample based on the plurality of time points of the interference information; and generating a spatial map of the frequency information indicative of the mechanical function of the biological sample. Generate.

In another embodiment, the present disclosure provides a method for obtaining image data and functional data from a biological sample, the method comprising using an interferometer to scan the biological sample along an imaging plane at multiple time points. obtaining radiation-based interference information provided by interfering a sample with a reference, wherein at least one axis of the imaging plane is an axis along at least a depth dimension or an axial dimension; processing the interference information to generate an image of the biological sample along the imaging plane with a processor configured to receive the interference information from an interferometer; determining frequency information reflecting temporal modulation induced by mechanical functions of the biological sample based on the plurality of time points of information; and, with the processor, a spatial map of the frequency information to the image. generating a spatial map indicative of the mechanical function of the biological sample.

In yet another embodiment, the present disclosure provides an apparatus for obtaining image data and functional data from a sample, the apparatus provided by interfering said sample with a reference along an imaging plane. 1. An interferometer for obtaining radiation-based interference information, wherein at least one axis of the imaging plane is an axis along at least a depth dimension or an axial dimension; a processor configured to receive interference information;
wherein the processor is configured to process the interference information to generate an image of the sample along the imaging plane and perform a frequency analysis of temporal variations induced by the sample; The analysis includes obtaining spectral information of the interference information, binning the spectral information into a plurality of frequency ranges, and a pseudo-color composite image including a plurality of colors corresponding to each of the plurality of frequency ranges. to generate a false-color composite image containing contrast portions corresponding to intracellular motion differences within the sample.

In yet another embodiment, the present disclosure provides an apparatus for obtaining image data and functional data from a biological sample, the apparatus comprising: at multiple time points along an imaging plane, the biological sample interfering with a reference; wherein at least one axis of said imaging plane is an axis at least partially along a depth dimension or an axial dimension. an interferometer; and a processor configured to receive the interference information from the interferometer, the processor processing the interference information to produce the image having a plurality of pixels along the imaging plane. generating an image of the biological sample; and using a time-frequency analysis of the temporal modulation of the interferometric information acquired at the plurality of time points, the temporal modulation caused by a mechanical function of the biological sample. It is configured to estimate a frequency spectrum for pixels and generate a report spatially mapping the mechanical features of the biological sample to the image.

In another embodiment, the present disclosure provides a method for obtaining image data and functional data from a biological sample, the method comprising using an interferometer at multiple time points in which at least one axis is in the depth dimension or obtaining interference information based on radiation provided by interference of the biological sample with a reference along an imaging plane that is an axis that is at least partially along an axial dimension; and extracting the interference information from the interferometer. processing the interference information to generate an image of the biological sample along the imaging plane, with a processor configured to receive; determining frequency information reflecting temporal modulation induced by mechanical features of the biological sample based on the plurality of time points of interference information; and using the processor, a spatial map of the frequency information to the image. generating a spatial map indicative of the mechanical function of the biological sample.

A more complete understanding of various objects, configurations and advantages of the disclosed subject matter can be obtained in view of the following detailed description of the disclosed subject matter with reference to the following drawings. Like numbers refer to like elements throughout the drawings.

Fig. 2 shows a Micro-OCT instrument configuration for use with various disclosed embodiments, where ao: analog output board, bs: beam splitter, imaq: image acquisition board, cl: camera lens, lsc: line scan camera. , smf: single mode fiber, pc: personal computer, clc: camera connection cable. 1 is an image of a human esophageal biopsy. Panel (A) shows an average standard micro-OCT image of 200 frames of biopsy. Panel (B) is a pseudocolor d-micro OCT image showing a number of different cells exhibiting various intracellular movement-related frequency components. Panels (C) and (D) are enlarged views of white ROI portions at the same locations in the samples shown in panels (A) and (B). Panel (E) is a magnified view of the yellow ROI of the d-micro-OCT image of panel (B), showing the cytoplasm (green), nuclei (red), and perinuclear regions (blue), which are All show different frequency ranges. Panel (F) shows d-micro-OCT images from the white ROI of panel (A), each corresponding to a different frequency range. Panel (G) is the corresponding histological image (H&E) of the biopsy sample. d-Micro OCT color code: red 0-0.08 Hz, blue 0.55-0.78 Hz, green 4.00-20.0 Hz. Bar indication: 100 μm unless otherwise specified. FIG. 3 shows intracellular dynamics probed over a wide frequency range. Panels (A)-(C) are frequency maps of the human esophageal biopsies shown in FIG. 2, 10 Hz in panel (A), 803 Hz in panel (B) and 2972 Hz in panel (C). Bar display = 100 µm. 1 is an image obtained from a human esophageal biopsy. Panel (A) shows an average micro-OCT image of 200 frames of biopsy. Panel (B) is a color-coded image of d-micro-OCT. Panel (C) shows the corresponding H&E stained histology of the sample. Panels (D)-(H) are enlarged views of the papilla delineated in panels (A) and (B). Panel (D) is a standard micro-OCT image of the nipple. Panels (E) and (F) are d-micro-OCT images of the nipple at 1.57 Hz and 16-20 Hz, respectively. Panel (G) is a d-micro-OCT pseudocolor composite image of the papilla. Panel (H) is a histological image of the papilla. p: papilla. d-Micro OCT color code: red 0-0.08 Hz, blue 0.55-0.78 Hz, green 4.00-20.0 Hz. Bar display in panels (A) to (C) = 100 µm. Bar display in panels (D) to (H) = 50 µm. 1 is an image of a human gastroesophageal junction biopsy. Panel (A) shows a 200-frame average micro-OCT image of the biopsy, showing columnar glandular architecture (arrows). Panel (B) shows color-coded d-micro-OCT images showing glands with cells in the low-frequency region below (b), cells with high-frequency modulation in the middle (m), and cells with low-frequency above. (a) and Panel (C) shows a comparison with biopsy histology showing mucous cells. d-Micro OCT color code: red 0-0.08 Hz, blue 0.55-0.78 Hz, green 4.00-20.0 Hz. Bar display = 100 µm. 1 is an image of a human neck biopsy; Panel (A) shows a 200-frame average standard micro-OCT image of a cervical biopsy. The basement membrane is indicated by the symbol bm. Panel (B) is the corresponding d-micro OCT image. The base layer (b) and the sub-substrate layer (pb) are clearly delineated from the intermediate and superficial layers. Panel (C) shows a d-micro-OCT image corresponding to the frequency range 0.47-0.63 Hz (blue channel in panel (B)) showing punctate areas in the cellular distribution (arrows). Panel (D) is the corresponding H&E histology. d-Micro OCT color code: red 0-0.08 Hz, blue 0.47-0.63 Hz, green 4.00-20.0 Hz. Bar display = 100 µm. 1 is a cross-sectional image of a human neck biopsy; Panel (A) is a static micro-OCT image of a neck biopsy. Panel (B) is the corresponding d-micro OCT image. The base layer (b) and the sub-substrate layer (pb) are clearly delineated from the intermediate and superficial layers. Panel (C) is the corresponding H&E histology. Panels (D)-(F) are enlarged views of a portion of the d-micro OCT image of panel (B), showing details of superficial cells in panel (D) and intermediate cells in panel (E); Panel (F) shows basal/subbasal cells. Orange arrows (with an asterisk " ^* " next to them) in panel (E) indicate cell nuclei (red) and white arrows indicate intermediate frequency components (blue) around the cell nucleus. d-Micro OCT color code: red 0-0.08 Hz, blue 0.47-0.63 Hz, green 3.98-20 Hz. Bars in AC, 100 μm; bars in DF, 25 μm. FIG. 2 shows several different d-micro OCT scan modes 1-5, mode (1): sequential acquisition of N d-micro OCT images, and mode (2): N scan points for each scan point. A-lines were collected, then the beam was moved to the next scan point, and so on until the entire cross-section was imaged. Mode (3): Raster dither the beam to obtain multiple mode (4): multiple beams are scanned (in series or in parallel) to acquire images at different imaging planes, mode (5): Simultaneous scanning of two beams offset by a distance d. Cross-sectional d-micro OCT images of freshly resected human and animal tissues. Panel (A) shows a biopsy taken from the cardia of the human stomach, showing cells in the low frequency region at the bottom (b), cells with high frequency modulation in the middle (m) and low frequency regions at the top (m). Fig. 1 shows a gland containing cells (a) and . Panel (B) shows a porcine liver with sinusoids, hepatocyte cords and hepatocytes in inset. Panel (C) shows the human hippocampus (CA3), showing dentate fascicles (d) and endplate (e) neurons. Panel (D) shows a mouse kidney tubule (t) and glomerulus (g). Panel (E) shows a porcine lymph node bleb, which contains multiple distinct lymphoid cells exhibiting unique frequency signatures. Bar display, 100 μm. Cross-sectional d-micro OCT images of human esophageal biopsies. Panel (A) is a pseudocolor d-micro OCT image showing multiple different cells exhibiting different intracellular movement-related frequency components. Panels (B)-(D) show intracellular dynamics probed over a wide frequency range, panel (B) at 10 Hz, panel (C) at 803 Hz and panel (D) at 2972 Hz. . It should be noted that cells of the same type exhibit different frequency signatures. Bar representation, 100 μm. d-Micro-OCT data obtained from B16F10 spheroids treated with toxic doses of staurosporine (STS) for 48 hours. Panel (A) shows untreated control spheroids and panel (B) shows spheroids treated with 5 μM STS. For spheroid d-microOCT, live cells are blue, apoptotic cells are green, and dead cells are red, based on previous validation data (not shown). Panel (C) Gold standard confocal microscopy and d-micro-OCT of Hoechst and propidium iodide stained spheroids of live/dead cell areas depending on drug concentration. is a bar graph showing a high degree of correspondence between In panels (A) and (B), 0 to 0.08 Hz for red, 0.47 to 0.63 Hz for blue, and 3.98 to 20 Hz for green. Bar display, 100 μm. Images of murine MC38 tumors immediately after resection treated with IMD containing doxorubicin (6 hours). Panel (A) is a d-micro-OCT image showing diffuse regions of intermediate frequency components (arrows) mirroring cleaved caspase 3 (CC3) IHC. Panel (B) shows the same spot in the sample. d-Micro OCT color code: red 0-0.08 Hz, blue 0.47-0.63 Hz, green 3.98-20 Hz. Bar display, 200 μm. Cross-sectional d-micro-OCT image of a murine liver immediately after resection. Panel (A) shows an untreated control liver at t=0 min and panel (B) shows the control liver at t=40 min with similar frequency content at both time points. Panel (C) shows the liver in 30 μm blebbistatin at t = 0 min and panel (D) shows the liver at t = 40 min where high frequency intracellular movements (green) dramatically increased after treatment. Decreased and increased low frequency intracellular movements (red). d-Micro OCT color code: red 0-0.08 Hz, blue 0.47-0.63 Hz, green 3.98-20 Hz. Bar display, 100 μm. Images of murine MC-38 tumors immediately after resection induced by interferon gene stimulators (STING, 48 hours). Panel (A) is a d-micro-OCT image showing mid-frequency blue cells (arrows) in the tumor, confirmed as infiltrating neutrophils by CD11b IHC at the same location in the sample in panel (B). be done. d-Micro OCT color code: red 0-0.08 Hz, blue 0.47-0.63 Hz, green 3.98-20 Hz. Bar representation, 100 μm. d-Micro OCT image of human in vivo skin obtained from the back of the forearm. The maturation pattern of the skin can be clearly seen in the transition from the stratum basale (b, blue) to the stratum corneum (sc, red). White arrows indicate the stratum granulosum (sg), which may become more active (blue) due to keratinogenesis occurring in this cell. The bottom image shows high-frequency cells in the stratum spinosum (ss) next to the stratum basale (orange arrows with an asterisk " ^* " next to them). High-frequency motion vessels (v, green) are also visible within the dermis. d-Micro OCT color code: red 0-0.08 Hz, blue 0.47-0.63 Hz, green 3.98-20 Hz. Bar representation, 100 μm. Cross-sectional d-micro OCT images of human esophageal biopsies. Panel (A) is a standard d-micro OCT image. Panel (B) is a magnified view of an area of panel (A), showing a cell with a central blue area (arrow) with weak representation of intermediate frequency components. Panel (C) is a super-resolution d-micro OCT image computed from the same dataset. Panel (D) is an enlarged view of a portion of panel (C) showing significant resolution enhancement. From this super-resolution d-micro OCT image, it can be seen that the blue intermediate frequency motion component (arrow) is present along a well-defined curvilinear path. d-Micro OCT color code: red 0-0.08 Hz, blue 0.47-0.63 Hz, green 3.98-20 Hz. Bar representation, 10 μm. Schematic representation of the DBPS-d-micro OCT system (top) and the endoscopic probe (bottom). Top: A conventional Michelson interferometer fiber-based spectral-domain OCT system with a fast fiber optic switch (FOS) in the sample arm that switches between two probe beams at the A-scan rate. SC LS - supercontinuum light source; FC - broadband fiber coupler; RM - reference mirror; FOS - fiber optic switch; IP - imaging probe; SP - spectrometer; PC - personal computer. Bottom: A mechanical linear pullback probe housing two separate optical micro-OCT assemblies, which achieves in-plane beam separation of the EDOF by MMF self-imaging. DS—drive shaft; SMF—single mode fiber; MMF—multimode fiber; SP—spacer; GL—gradient index lens; 1 illustrates an example system for obtaining image and functional data from living tissue of some embodiments of the disclosed subject matter; FIG. 1 illustrates an example of hardware that can be used to implement a computer and server of some embodiments of the disclosed subject matter; FIG. [0014] Figure 4 illustrates an example process for obtaining image data and functional data from living tissue of some embodiments of the disclosed subject matter;

Disclosed herein is a procedure using high-resolution (e.g., 1 μm axial resolution) micro-optical coherence tomography (micro-OCT) to obtain cross-sectional images of intracellular mechanical properties with dramatically enhanced image contrast. Embodiment. An embodiment of this procedure, termed “dynamic micro-OCT (d-micro-OCT),” acquires a time series of micro-OCT images and analyzes the temporal changes caused by intracellular movements separately or individually in each sub-region of the sample. This is done by performing a power frequency analysis of the variation, for example pixel by pixel or voxel by voxel. Disclosed herein are the results of d-micro OCT imaging of human post-resection esophageal and neck biopsy samples. From depth-resolved d-micro-OCT images of undamaged tissue, intracellular mechanical properties provide a novel contrast mechanism for micro-OCT that emphasizes subcellular-level morphological features and activity of epithelial surface maturation patterns. shown to do. In another embodiment, d-micro-OCT images are obtained by calculating changes in radiation phase at substantially the same location on the sample and at multiple different time points.

Despite significant advances in microscopy, many techniques currently in use provide static images of cells. Imaging living cells opens up a new dimension of functional evaluation that allows timely acquisition of pathophysiological information from molecular movements within organelles and cells, which was not possible with static snapshots. . An example of the state-of-the-art probing intracellular dynamics uses fluorescence microscopy techniques to track either fluorescently transfected organelles or actively perturbed microinjected fluorescent particles within cells. I have the technology. In addition to perturbing the biological system by adding exogenous labels when investigating intact tissue samples in which heterogeneous cell populations are present, the problem of interrogating large populations of cells is the scaling of these assays. has hindered

Accordingly, the devices, systems and methods disclosed herein provide improvements over current technology, including improvements in the areas of biomonitoring and diagnostics in various embodiments. Exogenous labels pose many practical impediments and can also perturb cells or tissues and alter the outcome, and the procedures disclosed herein introduce such exogenous labels. It facilitates the monitoring of cellular/subcellular structures of living cells and tissues without need.

Coherence-gating imaging techniques are microscopy techniques that overcome many of the above limitations. Coherence gating imaging techniques derive image contrast from tissue reflectance at refractive index interfaces within the sample, eliminating the need for additional labels. However, even a slight variation in the intracellular refractive index causes a small change in reflectance, which is difficult to see in one image, resulting in a relatively low image contrast. By exploiting the motion differences of different subcellular compartments, spatiotemporal signal analysis can be used to significantly enhance intracellular visualization, such as in dynamic full-field optical coherence tomography (FFOCT). FFOCT utilizes high power microscope objectives and low coherence optical interferometry to acquire high resolution lateral (Amphas) images of the natural native tissue reflectance at a given depth. By calculating the standard deviation or autocorrelation of the time-dependent signals generated by intracellular activity, Amphas dynamic FFOCT images can clearly capture intracellular features that are not well visible in static images.

Although dynamic FFOCT can provide a novel method of label-free cellular imaging, this technique is limited to image acquisition in the lateral plane. Depth-resolved cross-sectional images are highly desirable because many important tissues (e.g., epithelial tissues) mature vertically, and abnormalities in this maturation process are critical to understanding and diagnosing disease. . Optical coherence tomography (OCT) is a 10 μm resolution coherence gate imaging technique that can obtain cross-sectional images of tissue. Dynamic OCT, like dynamic FFOCT, measures changes in cross-sectional OCT images taken at the same location over a period of time. Dynamic OCT has been shown to provide information on tissue viability, mucus viscosity, cell migration, and remodeling. However, dynamic OCT cannot be used to see inside individual cells due to its relatively low spatial resolution.

Disclosed herein is a new technique called dynamic micro optical coherence tomography (d-micro OCT). Micro-OCT is very high resolution OCT with a resolution of about 2×2 μm (lateral)×1 μm (axial) or about 4×4 μm (lateral)×2 μm (axial). It was found that micro-OCT can visualize cross-sectional images of cells with unprecedented high definition. d-Micro-OCT provides novel microscopic information and contrast enhancement by performing pixel-by-pixel power frequency analysis of temporal variations caused by intracellular movements. Because d-micro-OCT prioritizes depth scanning, it has the unique ability to probe subcellular-level dynamics over a very wide (0-100, 0-1000, 0-10000, 0-100000 Hz) frequency range. have. The results obtained by d-micro-OCT using biopsy samples, spheroids, resected mouse tumor tissue, etc. have profoundly resolved intracellular mechanical properties of intact tissue and biomedical sciences. Showed to improve potential abilities.

In various embodiments, the methods disclosed herein can be performed using a micro-OCT device, such as the micro-OCT shown in FIG. OCT measures the electric field amplitude of light elastically scattered from tissue in three dimensions. Depth or axial (z) ranging is performed by interferometric measurements of the optical delay of light returning from the sample. In various embodiments, micro-OCT can be a spectral-domain OCT (SD-OCT) scheme, which detects the spectral interference of scattered light with a reference at all depths in parallel, followed by Fourier The analysis yields a depth-resolved scattering profile. In certain embodiments, the micro-OCT system and probes used differ from conventional OCT apparatus by incorporating a very broadband light source (e.g., a 800±150 nm laser-generated supercontinuum light source) and a common-path reference arm. to achieve 1 μm depth or axial (z) resolution. To achieve high lateral (x,y) resolution, a relatively high numerical aperture objective lens (numerical aperture=0.12) was used to focus the beam onto the sample. The probe beam focus was further adjusted by an annular apodizer to reduce the focal spot size from 2.4 μm to 2.0 μm. Apodization and wavelength dispersion extended the depth of focus to about 300 μm, enabling the high-resolution lateral imaging described above. In yet another embodiment, micro-OCT can be a swept-source OCT (SS-OCT) scheme, also known as optical frequency domain imaging (OFDI). In this alternative embodiment, the radiation from the broadband swept-source laser is split into a portion that falls on the reference and another portion that falls on the sample. The spectral interference pattern is detected by one or more detectors and Fourier transformed to obtain a depth-resolved reflectance profile. An SS-OCT image is created by scanning the beam over the sample and recording a depth-resolved reflectance profile. To achieve high lateral resolution over large depths to facilitate depth-resolved or transverse imaging, extended depth-of-focus methods similar to those used in SD-OCT (phase or amplitude apodization, sub-diffractive multifocal projection methods , axicon lens, etc.) are used.

FIG. 1 shows an example system comprising a spectral domain (SD) OCT imaging console and a common path benchtop probe. In the specific embodiment shown in FIG. 1, a supercontinuum light source (SuperK EXTREME, NKT Photonics) was used to illuminate a 50/50 beam splitter. Half of the source light was transmitted through the splitter to the benchtop probe. Light returned from the probe was relayed back to the spectrometer. The spectrometer consisted of a 940 lines/mm volume phase holographic transmission grating (Wasatch Photonics), a multi-element camera lens and a linescan camera (Basler Sprint, Basler). The camera pixels were used to detect the full spectral range of 800±200 nm with a full width at half maximum of 800±150 nm. As a result, the coherence length was 1 μm and the measured depth in the tissue was 1.25 mm. The detected signal was digitally converted with 12-bit resolution and transmitted to a computer at 20,480 lines per second (spectrum) via a camera connection cable and an image acquisition board (PCIe6341, National Instruments). The longest camera exposure time at this line rate was 48 μs. In the benchtop probe, the light beam was split into two wavefronts by a 45° rod mirror (NT54-092, Edmund Optics). The central circular wavefront was transmitted to the reference arm and the circular wavefront to the sample arm. The optical power at the sample was approximately 15 mW. Since the reference arm was equipped with the same optics as the sample arm, the dispersion was balanced. Light backreflected from the reference arm and light backscattered from the sample arm were coupled back through a rod mirror and guided back to the console by a single mode fiber (SM600, Thorlabs). Lateral (x,y) scans were performed using a pair of galvanometer scanners (Thorlabs) driven by an analog output board. The two-dimensional data size was 512×2500 voxels (x,z) and the corresponding reconstructed cross-sectional image size was 1 mm×1.25 mm (x,z). The x-direction galvanometric scanner scanned with either a sawtooth scan pattern at 40 Hz or a stepped scan pattern with 20.48 Hz increments. A three-dimensional micro-OCT dataset is obtained by acquiring time series of two-dimensional data at 512 cross-sectional planes spanning a lateral extent (y) of 1 mm.

Thus, in certain embodiments, an apparatus is provided for obtaining image data and functional data from biological tissue. The apparatus may comprise an interferometer for obtaining interferometric information based on radiation provided by interference of living tissue with a reference along an imaging plane. The apparatus can further comprise a processor configured to receive interference information from the interferometer. The processor can be configured to perform various steps, including processing the interferometric information to produce a morphological or phase image of the tissue along the imaging plane; determining frequency information from the temporal modulation of the interference information caused by the physical function; and generating a report or image that spatially maps the mechanical function of the living tissue. This report or image can be combined with the morphological image.

In various embodiments, the processor can further be adapted to identify temporal variations in interference information caused by mechanical functions of the biological sample. In other embodiments, the processor can be further configured to perform power frequency analysis of temporal variations to identify variations caused by intracellular motion of the biological sample. In still other embodiments, the processor can be further configured to perform said power frequency analysis on a pixel-by-pixel basis. In still other embodiments, the processor can be further configured to track the mechanical function of the living tissue over multiple longitudinal studies. In still other embodiments, longitudinal studies can include drug delivery to living tissue. In still other embodiments, the drug can be a chemotherapeutic drug. In other embodiments, the interferometer can be part of a micro-OCT system. In still other embodiments, the resolution of the interferometric information can be at least 2 μm×2 μm laterally or 1 μm axially. In yet another embodiment, the resolution of the interferometric information can be at least 4 μm×4 μm laterally or 2 μm axially (depth). In certain embodiments, the living tissue can be an in vivo tissue, while in other embodiments the tissue can be an ex vivo tissue or an in vitro three-dimensional cell culture. In some embodiments, processing the interferometric information to generate a morphological image of the anatomy along the imaging plane can further include compensating for motion artifacts, the compensating step comprising micro-OCT Applying local normalization and filtering to the intensity or phase values of pixels in a frame to generate processed frames, and unwarping transformation matrices (e.g., elastic unwarping) for each of said processed frames with or without a reference frame. warping, etc.) and applying this transformation matrix to the micro-OCT frames.

PCA Dimension Reduction and Machine Learning In various embodiments, methods are provided for obtaining image and functional data from living tissue. The method can include various steps, including obtaining interference information based on radiation provided by the interference of living tissue with a reference along an imaging plane, and processing the interference information. to generate a morphological image of the biological tissue along the imaging plane, obtain frequency information from the time modulation of the interference information caused by the mechanical function of the biological tissue, and combine the mechanical function of the biological tissue with the morphological image. generating a spatially mapping report.

Various embodiments disclosed herein use spectral analysis (e.g., power spectrum, standard deviation, variance, Fourier entropy, etc.) of temporal variations found in micro-OCT videos to generate cross-sectional cellular Data were generated that allowed investigation of mechanical properties. By appropriately binning the spectral data to produce a pseudo-color composite image, the disclosed method delineates cellular/sub-cellular level features in the transverse imaging plane without the need for any exogenous labeling. have been found to enhance our ability to characterize intracellular mechanical properties in tissues. This result highlights the ability of d-micro-OCT to investigate cross-sectional subcellular structures from the basal to superficial epithelial layers and the associated variations in cytomechanical properties and activity. will also become The above ability is of great significance because depth-dependent changes in cell and structural maturation patterns are fatal in the diagnosis of many epithelial diseases, including dysplasia and cancer.

In certain embodiments, d-micro OCT is used on freshly excised biopsy samples that are kept viable in culture medium. Images from such biopsies are difficult to register with histological images at the subcellular level, so some interpretation of behavior at the subcellular level is used preliminarily. Much remains to be learned about the mechanical origins of cell motility measured by this technique. There is evidence in the literature to support the idea that the dynamical perturbations described above, although random, are distinct from thermally driven Brownian motion. The dynamic perturbations described above are not Brownian motion, but are the result of ATP-dependent random force assembly that orchestrate cell motility among other biomechanical processes. In addition, there is also a need to achieve an improved understanding of the relationship between intracellular mechanical properties and their underlying physiological and pathophysiological conditions. Numerous studies, mostly using in vitro models, have investigated the origin of intracellular mechanical properties. Some of these studies have implicated dynamic signals in organelle trafficking and cell motility with contracted protein filaments, which have been shown to be transmuted by pharmaceutical agents. This result suggests that the physiological origin of the mechanical behavior of the probed subject can be exploited to inform disease state and its response to treatment. Due to its high resolution in all three dimensions, d-micro-OCT is well suited for conducting the above investigations. However, to fully understand the utility of the information provided by d-micro-OCT, the identification/tuning of specific intracellular molecular motion-dependent mechanisms will be necessary to better understand different tissue/cell types in different conditions. Much research is needed.

While intact tissue is an excellent substrate for d-micro-OCT, embodiments of the disclosed procedures are suitable for other additions including 2D cell cultures, 3D cell cultures, spheroids, organoids, and organ chips, among others. It may also have excellent utility in analytical assays. The advantage of d-microOCT as a cell viability assay is that the platform can identify the metabolic or pathophysiological state of cells without sample destruction for dead cell staining or cell type characterization. be. Such use of d-micro-OCT may improve the efficacy of multi-cell assays currently under development.

Another application of d-micro-OCT is in vivo implementation. Presently, the use of micro-OCT within the nasal cavity of living human patients has demonstrated the unique ability to study the mechanical properties of cilia and mucus. d-Micro-OCT becomes more difficult to perform in vivo due to the longer imaging duration (several seconds) during which the patient must be stabilized at the subcellular level. This problem can be overcome by techniques such as intimate coupling of tissue and micro-OCT probes, opening up new label-free options for achieving high cellular contrast and functionally informed optical biopsies. be done.

Materials and procedures for practicing various embodiments are described below, along with descriptions of example results.

Tissue Preparation and Imaging In certain embodiments, human upper gastrointestinal and neck biopsies were obtained from study participants. Each biopsy was immersed in cell culture medium immediately after excision to preserve tissue viability. In preparation for d-micro-OCT imaging, the biopsies were mounted on glass slides and the luminal side of the tissue was tilted with respect to the imaging beam using a dual-axis goniometer to avoid specular reflections. All imaging was performed at ambient temperature (25° C.), and samples were kept moist by adding small amounts of cell culture medium to the tissue throughout the imaging session, if necessary. Despite all efforts to keep the sample well wet, image artifacts due to water evaporation were sometimes noticeable in the real-time display of the images, especially when the biopsy samples were small. Therefore, certain samples were run with the luminal side of the tissue in contact with the glass slide to minimize water loss during imaging. After d-micro-OCT imaging, the imaged areas were labeled with dye before being fixed with formalin. The samples were then processed to obtain 5 μm thick hematoxylin and eosin (HE) stained tissue slides corresponding to the areas imaged by d-micro-OCT.

Embodiments Providing High Spatial and Temporal Resolution Dynamic Micro-OCT Imaging Various embodiments of the present application provide systems and methods for probing mechanical properties at the subcellular level in the transverse plane. Such a cross-sectional imaging system can have an axial resolution of less than 2 μm and a lateral resolution of less than 5 μm. The method can include acquiring multiple images, regions, lines, or points over time. A power spectral density measurement can be made for each pixel in the image. Example embodiments include time-frequency analysis (e.g., short-time Fourier transform, Wigner transform, wavelet transform, etc.) at every pixel of an image, or power spectrum estimation techniques (e.g., Welch's method, etc.) at every pixel of an image, or local Other techniques are included to compute frequency information over a period of time. An alternative embodiment is determining the entropy of the frequency spectrum or other technique for quantifying the frequency content of the signal.

Two beam scanning embodiments are disclosed. One scheme can probe the frequency components at a rate up to half the scan rate of the galvanometer mirror, which in one embodiment ranges from about 1-40 Hz. In another embodiment, the mirror stops at one spot and acquires data at the A-line rate. This A-line rate is about 1-20 kHz in one example, and can alternatively be about 1 kHz-3 MHz.

Microscopy, Data Acquisition and Data Processing A benchtop micro-OCT system microscope was used to provide cross-sectional images of intact tissue at a resolution of 2 x 2 μm (lateral) x 1 μm (axial) to a depth of 300 μm. Imaging was performed using an A-line (depth dependent reflectance profile) rate of 20.48 kHz and a 1 mm lateral scan was achieved by scanning the beam over the sample using a galvanometer mirror. Identification of viable portions of tissue samples was facilitated by using a custom-written data acquisition program capable of real-time display of image contrast based on pixel-by-pixel standard deviation approximations. d-Micro OCT images were obtained using two different imaging modalities, each probing the mechanical properties in different frequency ranges. The first approach, called multi-scan (MS) d-micro OCT, involved repetitive scanning of an imaging beam laterally over a region of interest at a frequency determined by the galvanometer scan rate over a period of time. To probe frequencies up to 20 Hz, the imaging beam was repeatedly scanned at 40 Hz over a 1 mm region of interest, typically for a duration of 25 s (to avoid aliasing phenomena). This resulted in 1000 cross-sectional images of the same location, each of which consisted of 512 A-lines. A second imaging modality, called single-scan (SS) d-micro OCT, traverses the imaging beam in steps over the lateral range of interest, stopping at equally spaced positions each time, with a rate determined by the A-line rate. It was to acquire the series of the A line with. To match the region of interest of the imaged object with the first scheme, it took 25 s to acquire 1000 A-lines at 512 discrete positions at 20.48 KHz over a range of 1 mm (i.e., with the previous scan scheme an equal amount of time). SSd-microOCT can probe cell dynamics two orders of magnitude higher than the first scheme, although with lower frequency resolution. By acquiring temporal data over a longer period of time, higher frequency resolution can be achieved. To probe frequencies up to 10.24 kHz, the imaging beam was stepped across the same 1 mm lateral range, stopped at 512 equally spaced positions, and 1000 A-lines were scanned at a rate of 20.48 kHz. Acquired. Both of the above scanning procedures took the same amount of time (25 s) to complete. In various embodiments, frequency was probed based on time points in the range 0 Hz to 50 Hz and collected at each location or sub-region (eg, each pixel and/or each voxel) within a sample up to 100 Hz. In general, frequencies were probed at half the frequency of the data collected to avoid aliasing phenomena.

In any imaging method, the variation of the micro-OCT signal intensity with respect to time was analyzed for each pixel of the cross-sectional image. In yet another embodiment, noise can be reduced by processing and averaging adjacent pixels. An estimated power spectrum was obtained for each pixel of the micro-OCT dataset by performing a short-time Fourier transform (STFT) using Welch's method, assuming that the fluctuations are ergodic processes. In this approach, time-dependent data were analyzed in shorter segments rather than in their entirety.

の分散を決定した。最初に、平均減算された各セグメントにハニング窓ｗを適用してから、その後にゼロパディング処理して要素数５１２のアレイＳ_ｘ，ｙを得る処理ステップを使用した。次に、各セグメントに離散フーリエ変換（ＤＦＴ）を適用し、得られた結果の平均をとって当該各画素におけるパワースペクトルを得た。横断面平面において各画素ごとに上記処理を行うことにより、Ｍ＝２５６個の等間隔の周波数ビン（ｆ_ｘ，ｙ）を有するハイパースペクトルデータセットが得られた。

Despite the reasonably short d-micro-OCT imaging time (25 s), images of tissue subjected to the first beam scanning scheme showed spatially dependent nonlinearity on the order of tens of micrometers. Motion drift has occurred. Tissue motion is thought to be due to evaporation and/or thermal expansion, resulting in tissue precipitation during imaging. To compensate for such motion artifacts, micro-OCT frames were subjected to local normalization and Gaussian filtering. From this processed frame, the elastic unwarping transformation matrix was calculated with reference to the center frame (500). This transformation matrix was then applied to the original micro-OCT data. An estimated power spectrum of each pixel in the micro-OCT dataset was then obtained by performing a short-time Fourier transform with Welch's method, assuming that the fluctuations are ergodic processes. With this approach, time-dependent data were analyzed in shorter segments rather than in their entirety. By processing a plurality of segments each ⅕ (L=500) in length of the total temporal data, each having a 50% overlap with the next segment, a plurality of corrected The periodogram _Ik was derived. For a given length of recorded data, the length of each segment and the amount of overlap are the resulting spectral resolution and the estimated power spectral density.

We determined the variance of First, a processing step was used that applied a Hanning window w to each average-subtracted segment, followed by zero padding to obtain a 512-element array S _x,y . A Discrete Fourier Transform (DFT) was then applied to each segment and the results obtained were averaged to obtain the power spectrum at each pixel of interest. Performing the above processing for each pixel in the cross-sectional plane resulted in a hyperspectral data set with M=256 equally spaced frequency bins (f _x,y ).

Based on the above data acquisition parameters, the estimated power spectrum was calculated from the nine averages, and this equivalent factor effectively reduced the variance of the estimated power spectrum.

For both imaging schemes, similar processing was applied to the resulting time-dependent data to yield 256 equally spaced frequency bins ranging either from 0 to 20 Hz or from 0 to 10.24 kHz. rice field. Although the latter imaging method has lower frequency resolution, it enables probing of cell dynamics that is two orders of magnitude higher than the former imaging method.

はさらに３つの周波数範囲にビニングされ、それぞれにＲＧＢ画像においてカラーチャネル（例えば、赤色：０～０．０８Ｈｚ、青色：０．５５～０．７８Ｈｚ、緑色：４．００～２０．００Ｈｚ等）を割り当てて、最終的なｄ－マイクロＯＣＴ画像表現を作成した。特定の実施形態では、組織のサブセルラーレベルの特徴及び細胞の特徴が際立つように周波数範囲カラー割り当てを経験的に選択し、これにより周波数カラー割り当ては各サンプルの間で変わる。ここに提示した全てのマイクロＯＣＴ画像及びｄ－マイクロＯＣＴ画像は、正確な表現を保証し、対応する組織学的像との対比を容易にするため、組織屈折率を１．４と仮定して、等方性の画素アスペクト比を達成するためにスケーリングされている。画像再フォーマット及び解析はすべて、ImageJ及びMatlab（Mathworks Inc.）を用いて行った。 In order to be able to directly visualize the distribution of dominant fluctuation frequencies in the tissue,

is further binned into three frequency ranges, each with a color channel (e.g., red: 0-0.08 Hz, blue: 0.55-0.78 Hz, green: 4.00-20.00 Hz, etc.) in the RGB image. assigned to create the final d-micro OCT image representation. In certain embodiments, frequency range color assignments are empirically selected to accentuate sub-cellular and cellular features of tissue, whereby frequency color assignments vary between samples. All micro-OCT and d-micro-OCT images presented here assume a tissue refractive index of 1.4 to ensure accurate representation and facilitate comparison with corresponding histological images. , has been scaled to achieve an isotropic pixel aspect ratio. All image reformatting and analysis were performed using ImageJ and Matlab (Mathworks Inc.).

In various embodiments, the fluctuating frequency information can be binned or divided into any number of subgroups (eg, from 2 to 10 or more subgroups, etc.), with each subgroup represented by can be displayed differently on the organization's map (as shown). The number of different frequency ranges in which different anatomy can be visualized is an important consideration in choosing the number of frequency bins used to visualize d-micro OCT images. The frequency bins can also be based on power spectral maxima or power spectral patterns. Spatiotemporal analysis of frequencies is also possible, e.g., one frequency pattern or peak in a nucleus is next to or remote from that nucleus, or the same frequency pattern present in the membrane of a cell or organelle. They can have different biological meanings.

Identity and maximum number of frequency range subgroups or peaks, and the most meaningful frequency range or peak can be determined by principal component analysis (PCA), kernel PCA, linear discriminant analysis, generalized discriminant analysis, UMAP, or t-SNE, etc. can be determined using the dimensionality reduction technique of For example, each principal component image, corresponding to principal components representing the majority (eg, 80% or 90%) of the power spectrum variance, may correspond to different frequency patterns representing different regions of motion in the image. Similar to the use of frequency bins corresponding to the R, G, and B channels of a color image as described above, it is also possible to create a multicolor image using PCA principal component weighted images. Other dimensionality reduction techniques can also create multi-dimensional images using a similar approach.

Image Registration Since the spectral analysis for d-micro-OCT processing is performed pixel by pixel, motion unrelated to bulk motion or intracellular or membrane perturbation motions occurring during image acquisition must be corrected. An image registration algorithm was applied to correct for in-plane bulk motion artifacts as needed. The amount of lateral shift required for the image registration process was measured by performing a two-dimensional cross-correlation between consecutive image frames. After performing the bulk motion correction, the motion correction can be further refined by performing local unwarping with an affine transformation.

Attenuation Correction A depth-dependent attenuation of the micro-OCT intensity occurs along the optical axis, gradually reducing the subsurface information down to the noise level. An algorithm was developed to amplify the micro-OCT signal along depth with attenuation correction in image post-processing. Algorithms for such depth dependent correction include exponential fitting, reslicing→image normalization or equalization processing→reslicing, or time gain or depth gain adaptive compensation.

Image registration and attenuation correction were performed, if necessary, before the power spectrum was calculated for each pixel of the cross-sectional image.

The spectral information is binned into multiple frequency ranges and the results are color coded to create a pseudocolor composite. This facilitates visualization of cellular structures that could not be discerned in static micro-OCT images.

Image registration is another embodiment in addition to methods that correct for specimen motion during an acquisition sequence to improve the quality of results.

Attenuation correction of the micro-OCT signal along the optical axis is another embodiment that improves the quality of results by correcting for intensity fluctuations caused by overlying optical properties.

During data acquisition, real-time moving standard deviation estimation was performed to identify viable regions exhibiting variations in cytodynamic properties in samples.

Report generation After generating a morphological image and/or a frequency map based on the morphological image, a , reports can be generated and forwarded. The report may include image data, such as morphological images or frequency maps, and may also include information regarding characteristics or diagnoses identified based on the images, maps, or diagnoses. Reports may be transferred in a variety of forms, including electronic transmission and/or hard (eg, paper, etc.) copies. Reports can be forwarded to subjects such as clinical staff (eg, doctors, nurses, technicians, or other medical personnel), researchers, patients, or healthcare providers.

Functional imaging by d-micro-OCT was performed on biopsies obtained from subjects diagnosed with multiple different esophageal disorders. FIG. 2 shows images of an esophageal biopsy taken from a subject undergoing endoscopy for chronic gastroesophageal reflux disease (GERD). In the average (n=200) standard micro-OCT image shown in FIG. 2A, some structures can be seen but were not very noticeable due to poor refractive index contrast. In addition, the presence of speckle further hindered visualization of cellular features in detail. This speckle is a common source of noise in OCT images even after frame averaging. Significantly higher cell contrast was achieved with d-micro-OCT (Fig. 2B). This contrast enhancement is due to differences in subcellular-level motion of separate tissue compartments, with cytoplasmic light-scattering motion associated with higher frequencies than those of cell membranes and interstitial spaces. Organelles such as cell nuclei were also identifiable using d-micro-OCT due to their low frequency content (Figs. 2D, 2E). Some cells show fluctuations spanning a moderate frequency range (Fig. 2E, blue; 0.55-0.78 Hz) around the cell nucleus (Fig. 2E, red), probably due to small It is thought that this indicates motion in the vicinity of the organ. Using power spectral analysis and selecting appropriate frequency ranges from the resulting hyperspectral data provided a means of selectively visualizing multiple different tissue structures. For example, as shown in FIG. 2F, the cell membrane appeared brighter than the cytoplasm at 0.16 Hz, but the contrast was reversed in the higher frequency range of 4-20 Hz. Besides providing detailed microstructural information comparable to the corresponding HE-stained histological image (Fig. 2G), d-micro-OCT also provides information on multiple different intracellular motility rates in single cells. . For example, some squamous epithelial cells on the basal layer exhibited higher frequency components than others (Figs. 3A-3C), indicating heterogeneity of intracellular movements even within the same cell type. .

FIG. 4 is an image of an esophageal biopsy. In standard micro-OCT images, the papilla (Fig. 4A,p) extending from the base of the biopsy can be seen, but is otherwise homogeneous and overall, with no clearly distinguishable cellular features. nothing was seen. In contrast, the pseudocolor d-micro OCT image (FIG. 4B) revealed many additional morphological and functional details not visible in the standard micro-OCT image. Using d-micro-OCT, the identification of individual squamous epithelial cells (Fig. 4B) was facilitated, and their appearance was very consistent with that of squamous epithelial cells in the corresponding histological images (Fig. 4C). Furthermore, stratification of intracellular mechanical properties also revealed that maturation is depth-dependent, with mature cells in the outermost layer exhibiting slow motility, whereas those deeper in the biopsy were less active. Mature cells exhibited faster intracellular movements. These findings support our understanding of squamous epithelial maturation: cell division and maturation depend on the depth from the basal layer to the surface, and cells may be dead or detached at the top. is consistent with the understanding that Another feature that was made more pronounced by d-micro-OCT was flat papillae. In a standard micro-OCT image (Fig. 4D), cells around large papillae can be faintly observed with brightness comparable to that of surrounding squamous epithelial cells. In d-micro-OCT, the large peripapillary cells were prominent in low frequency spectral images such as the 1.57 Hz image shown in FIG. 4E. It was also revealed that there were other cell types within the papilla that exhibited activity in the higher frequency range of 16-20 Hz (Fig. 4F). The corresponding histological picture (Fig. 4H) revealed the presence of possible intra-papillary leukocytes and other possible basal epithelial cells among such cells. Suggest.

This ability of d-micro-OCT to highlight cell structures that were not visible with micro-OCT is illustrated in detail in an example of a biopsy taken from the gastroesophageal junction of a human subject (FIG. 5). A standard micro-OCT image (Fig. 5A) shows glandular structures with difficult to discern intracellular contrast. d-micro-OCT highlighted low-frequency basal structures consistent with the apical mucin cytoplasm (Fig. 5B, a) and cell nuclei (Fig. 5B, b). Interestingly, the center of the cell showed a large amount of high frequency components (Fig. 5B,m). Corresponding histological images (Fig. 5C) confirmed that these glands contained mucin cells.

Imaging the cervical squamous epithelium by d-micro-OCT provided additional findings (Fig. 6). In the frame-averaged standard micro-OCT images of the cervical squamous epithelium, a few squamous cell basement membranes (Fig. 6A, bm) and contours were observed (Fig. 6A). The corresponding d-micro-OCT image showed detailed features of most of the cells within the squamous epithelium throughout the cross-sectional image (Fig. 6B). The basal and subbasal cells of the lower quarter of this epithelium (FIGS. 6B,b,pb) exhibit a substantial 0.47-0.63 Hz frequency component (blue in FIGS. 6B, 6C), It was present throughout the cytoplasm. Intermediate cells exhibited such mid-level frequency components, which were mainly present only in the center of the cells (Fig. 6C, arrows). Signals below 0.47-0.63 Hz become progressively smoother as the cells mature towards the surface (Fig. 6C). Low frequency components (0-0.08 Hz) dominated near the surface (Fig. 6B, red). These observations are highly consistent with the corresponding histological image shown in FIG. 6D, highlighting features of epithelial maturation characterized by different intracellular mechanical properties depending on depth.

Examples The following are non-limiting examples of procedures that can be performed using any one or more of the disclosed devices, methods or systems.

Imaging and Identification of Cells d-Micro-OCT imaging of freshly excised human tissue can be performed and registered cell-by-cell to tissue slides. With corresponding HE and immunohistochemistry techniques (IHC) as ground truth, available d-micro OCT machine learning algorithms can be trained/validated to detect different cell and tissue types in humans. .

The ability of d-microOCT to distinguish live/apoptotic/dead cells in three-dimensional culture is achieved by treating spheroids from multiple different human and murine melanoma cell lines. d-Micro-OCT was performed on control and treated spheroids and the results are the gold standard of cell viability performed using Hoechst-33342, annexin V-FITC, and propidium iodide (PI) staining. A comparison can be made with fluorescence-based assays. In such cases, live spheroidal tumor cells exhibit a moderate frequency motion component, apoptotic cells exhibit a high frequency motion component, and dead cells exhibit a low frequency or no motion signature. , we discovered.

Dynamic micro-OCT imaging can be performed on an implantable microdevice-treated post-ablation human tumor mouse model and compared to a panel of established IHC markers associated with drug efficacy. This data will define drug- and tumor-dependent d-micro-OCT signatures associated with apoptosis and cell death in cancer tissues.

Human tumor mouse model tissues can be mutated by well-characterized pharmacological inhibitors of molecular processes (cytoskeleton, metabolism, growth) associated with cell proliferation. Changes in tissue/cell frequency components depending on the drug's mode of action reveal the mechanism underlying the d-micro OCT signal.

Improving image quality and characterization of intracellular motion Imaging system improvements, including the use of broader band light sources, increased numerical apertures (NA), and extended depth-of-focus optics, have improved spatial resolution in all dimensions. It can be increased by a factor of two. Sub-pixel level super-resolution temporal extrapolation algorithms can be used to further improve spatial and frequency resolution.

Using time-frequency analysis and machine learning algorithms, the d-micro OCT data can be drilled more extensively to discover additional biomedical relevant information within the motility signals.

Stabilization, out-of-plane 3D scanning, fast phase-sensitive d-micro OCT methods can be developed to reduce motion artifacts that occur when imaging biological systems. To fabricate two probes (handheld and endoscopic) to perform d-micro-OCT imaging in patients and to examine the feasibility of using these two instruments in vivo in the skin and upper gastrointestinal system in a clinical pilot study. can be done.

In various embodiments, dynamic micro-OCT can be used to measure intracellular motion in cross-sectional images of freshly resected human tissue. Dynamic micro-OCT (d-micro-OCT) was used to calculate pixel-by-pixel temporal power spectra of sequentially acquired micro-OCT images, encoding different frequency bands into separate RGB color channels (Fig. 7B). The results showed a significant increase in image contrast, revealing different cellular and subcellular-level features at different characteristic frequencies (Fig. 7B) not seen in conventional micro-OCT intensity images (Fig. 7A). As can be seen from the human cervical image (Fig. 7), detailed cell morphology was distinguishable using d-micro OCT (Fig. 7B) and static cell nuclei (Fig. 7E, marked with an asterisk " ^* "). marked by the presence of a relatively static plasma membrane surrounding a variable cytoplasm containing (orange arrows). Cross-sectional and functional characterization of d-micro-OCT allows the maturation pattern to be clearly recognized by the basal/sub-basal cells exhibiting intermediate frequency content (Fig. 7F, blue), which in some cases is caused by organelle transport or microtubule polymerization that decreases and integrates around the cell nucleus (Fig. 7E, white arrows) as cells migrate to the middle layer. This mid-frequency modulation is absent in the superficial layer (Fig. 7D). Because d-micro OCT images are depth-first and fast acquired, a much larger range of frequencies can be probed compared to imaging modalities in the transverse plane (FIG. 9). This high-speed, sub-cellular resolution, cross-sectional imaging capability of d-micro-OCT makes it ideal for investigating the utility of dynamic microstructural information contained in cells in tissues.

Embodiments of the disclosed procedures can be used to establish a label-free depth-resolved microscopy imaging method for phenotyping of cells in tissue based on intracellular motility. This has been achieved by deepening our understanding of the biological and clinical significance of d-micro-OCT and by developing and validating advanced novel d-micro-OCT techniques with dramatically improved capabilities. The potential impact that d-micro-OCT can have on various biomedical fields is discussed below.

Biomedical imaging micro-OCT is poised to redefine the field of intravital microscopy due to its ability to obtain cross-sectional images of tissue at 1 μm resolution and its relative ease of implementation in living patients. It is a rapidly growing technology that is Micro-OCT has so far been performed in vivo using small diameter probes, has various technical implementations, and various organ systems/diseases are being investigated. Phenotyping of histomechanical properties is also becoming prevalent in conjunction with the growing need for label-free cell assessment for drug development and personalized therapeutic assays. By fusing micro-OCT and intracellular motility phenotyping, d-micro-OCT will accelerate both fields and provide powerful and novel biomedical imaging capabilities with great impact.

Intravital microscopy. d-Micro-OCT can overcome many limitations of tissue resection and histopathology. d-micro OCT has cell resolution approaching the state-of-the-art HE, enhanced contrast and cross-sectional imaging (Fig. 7), and d-micro OCT is obtained from static tissues on microscope slides. Contains feature information that cannot be We used small diameter micro-OCT probes in vivo in the human body and performed d-micro-OCT in live patients using the advanced techniques disclosed in this application. The ability of d-micro-OCT to assess cell structure/function in vivo in cross-sections allows for less invasive and less sampling error diagnostics, allowing interventions in real-time, It can be done with higher precision.

3D Cell Models New techniques for growing patient cells into 3D cell models (such as organoids, spheroids, or organ chips) are gaining importance in preclinical drug screening. This is because the cell heterogeneity and function of the original tissue can be recapitulated better than 2D culture. Commercially available assays generally require cells to be killed, stained for viability, and counted. d-Micro-OCT, like other high-resolution photometabolic imaging techniques, can be used to longitudinally numerically assess viability, cell type, and cell state without killing the cells. In addition, d-micro OCT has a large field of view and a depth resolution of 1 μm, a feature that can enable accurate and efficient 3D cell imaging in an organotypic culture platform.

individualized treatment. Many different classes of drugs with a wide variety of mechanisms of action are available or under development for treating patients. However, existing approaches measure efficacy in terms of specific mechanisms of action of drugs in living tissues (e.g., cell viability, activation of immune cells, changes in metabolic rate, or cell division and proliferation). It does not provide a good method for doing so. Some clinical trials are limited to individual time points with repeated biopsies and static readings to obtain the above information. In vivo d-micro-OCT allows continuous monitoring of diseased tissue to optimize individualized therapy, thus providing earlier and more direct indication of whether a compound has the desired effect. can be used to provide a means for specific identification.

Implantable Microdevices (IMD) IMD is a novel technology for simultaneously monitoring the effects of multiple therapies in a patient. The device has multiple separate wells, each containing a different drug/cocktail. After implantation and incubation in the patient's tumor, the device and adjacent tissue are surgically removed and subjected to IHC histopathology to quantify necrosis. Alternatively, the IMD can be routinely examined in vivo by d-micro OCT and functional microscopic readout can provide critical cross-sectional imaging information. Real-time continuous monitoring of drug effects, like systemic therapy, can significantly increase the impact of this technology over the currently available discrete time point approaches.

Micro-OCT is enabled by a combination of broadband supercontinuum light sources, common-path interferometry, and unique extended depth-of-focus imaging. d-Micro-OCT is a new technology that develops conventional micro-OCT and provides unprecedented label-free images of cross-sections of cells with more meaningful contrast.

Biological Significance of Dynamic Signals: A Key Innovation of the Present Disclosure is Histological Contrast (IHC) Consistent d-Micro OCT Human Cell and Tissue Atlases Across Different Cell Types, Tissue Types, and Pathology Types is the creation of By analyzing this data set, the diagnostic accuracy of d-micro-OCT for many important diseases can be determined. Examinations in spheroids and human mouse tumor models provide insight into the ability of d-micro-OCT to quantify response to treatment and characterize different mechanisms contributing to intracellular motility. Taken together, the experimental set described above can significantly advance the boundaries of knowledge in the field of intracellular motility imaging.

Expanding the capabilities of d-micro OCT: New techniques are being developed and validated to maximize information extraction from intracellular motility signals and in vivo imaging to improve spatial and temporal resolution. Such state-of-the-art technology could make d-micro OCT much more sophisticated and more widely applicable than it is today. These innovative developments are detailed below.

d-Micro-OCT enables cross-sectional motility imaging of intact human and animal tissues ex vivo, at high-contrast, sub-cellular levels. Using a micro-OCT benchtop system microscope to obtain cross-sectional images at a resolution of 2 μm × 2 μm (lateral) × 1 μm (axial) using a supercontinuum-illuminated common-path spectral-domain OCT (SDOCT) system, d - Micro-OCT data were obtained (Figures 7, 9-16). The depth of focus was 300 μm, which was enhanced by a circular apodization of the objective lens of the sample arm. Images were acquired with an A-line (depth-resolved reflectance profile) rate of 20 kHz and a lateral spread of 1 mm. All tissue samples were imaged fresh at 25° C. in culture medium (FBS/DMEM 10%+penicillin-streptomycin 1%). 1) a scan mode (Fig. 8, scan mode 1) that sequentially acquires N = 1000 images (512 A-lines per image) over a period of 25 seconds, or 2) N = 1000 at each lateral scan point. Two scan modes (FIG. 8, scan mode 2) for sequentially acquiring A lines were used. After local elastic unwarping to remove bulk motion due to evaporation or vibration, power spectrum estimates (periodograms) were computed pixel-by-pixel using Welch's method and frequency normalization. In scan mode 1, in order to express data in one image, frequency components of low frequency (approximately 0 to 0.1 Hz), medium frequency (approximately 0.5 to 0.7 Hz), and high frequency (approximately 4 to 20 Hz) Images were merged into the RGB channels of a 24-bit color image. Figures 7 and 9-16 highlight the dramatically improved contrast and functional information content that d-micro OCT provides over a large frequency range for a wide variety of different tissue types. Important findings include:
1. d-micro-OCT provides cross-sectional images of subcellular motility with improved cellular contrast over standard micro-OCT for a wide variety of tissues (Figs. 7, 9-16).
2. Subcellular features can be visualized based on modulation frequency discrimination (Figs. 7, 9, 16).
3. Spatiotemporal information can be used to encode cell type and/or cell state (Figs. 7, 9-15).
4. The ability of d-micro OCT to perform cross-sectional imaging and frequency encoding allows us to observe the functional maturation of epithelial cells from the basal layer to the surface (Figs. 7, 10, 15, 16).
5. d-micro OCT allows investigation of previously unrecognized high frequency components in the kilohertz range (FIG. 10).
6. d-micro-OCT can show differential changes in intracellular modulation suggestive of cell viability after application of therapeutic agents to spheroids (FIG. 11) and tissues (FIGS. 12, 13).
7. d-micro OCT highlights inflammatory influx (FIG. 14) caused by chemotherapy drugs.
8. d-micro OCT enables high-contrast in vivo functional imaging of human skin (Fig. 15).

Identification of the biological significance of d-micro OCT in cells and tissues

Determining the Accuracy of d-Micro OCT for Discriminating Clinically Important Human Cell/Tissue Types

Initial data (FIGS. 7, 9-16) show that d-micro OCT provides significantly improved morphological contrast over conventional micro-OCT (such as FIG. 7A), although d-micro OCT It is unclear to what extent different human cell and tissue types can be distinguished. An atlas can be generated from d-micro-OCT images of clinically significant human tissues that are consistent with histology/IHC at the cellular level. This data can be used in available machine learning pipelines to determine the accuracy of d-micro-OCT for microscopic morphological diagnosis. The results obtained thereby form the basis for performing d-micro-OCT for pathological diagnosis of human tissues.

表１．分析対象の組織種類 Specimen type and number. Tissues from normal epithelium, dysplastic epithelium, malignant epithelium, solid tumors, and lymph node biopsies and human surgical specimens (Table 1). These tissue types can be used based on their availability, clinical relevance, and diversity of cell types involved. A number of different sample sizes (eg, number of specimens per tissue type, n=10, etc.) can be used from different patients. As many as ten d-micro OCT sections can be imaged, processed and analyzed per specimen.

Table 1. Tissue type to be analyzed

d-Micro OCT Imaging First, imaging can be performed using conventional benchtop d-micro OCT as described above, but advanced d-micro OCT techniques can be incorporated. Imaging can be performed less than an hour after resection in fresh tissue. Specimens can be cut into 200 μm thick sections, placed in specimen holders that promote diffusion through the cut surface, and immersed in culture medium. The tissue can be imaged at ambient temperature of 37° C. with the epithelial surface facing upwards (where this orientation is relevant) to facilitate imaging. After imaging, the location of d-micro-OCT cross-sections in the tissue can be marked by precision laser ablation on top and bottom of the sample. Additionally, laser ablation can be overmarked with histologically visible fluorescent and/or absorbing inks.

表２．調査で使用された細胞種類及びＩＨＣマーカの例 Histological Processing Tissues can be formalin-fixed paraffin-embedded (FFPE) processed. 3D serial sections can be cut every 4 μm. Virtual HE (VHE) staining can be computed from autofluorescence images input to a convolutional neural network (CNN) trained with a generative adversarial network (GAN). Sections can then be treated with DAPI staining (to label cell nuclei) and tissue-based cyclic immunofluorescence (t-CyCIF) can be performed. Cell types of interest can be identified by special IHC markers, examples of which are shown in Table 2. The 3D dataset can be registered, warped, and reformatted so that there are top and bottom marks when matching d-micro OCT and VHE/IHC images. Additional dataset warping processing can be performed so that the same cells are seen in both VHE/IHC and d-micro OCT images.

Table 2. Examples of cell types and IHC markers used in the study

Analysis of cell types. The d-micro OCT periodogram image can be computed using standard procedures or more sophisticated algorithms and can be input into a custom CellProfiler machine learning pipeline. Training and refinement can generate rules using CellProfiler Analyst with the t-CyCIF and VHE determined cell types as ground truth. Classification accuracy can be performed prospectively.

Histopathological analysis When blinded to pathologists, 1) based on d-micro OCT images and 2) based on d-micro OCT images overlaid with cell type classification, d - Consistent diagnosis can be made on micro-OCT sections. The gold standard VHE diagnosis can be used to measure sensitivity/specificity for each tissue/disease type.

Statistical Basis for Number of Samples. Given the number of images per tissue type of 100 and the minimum number of cells per image of 100, it is expected that the number of cells identified by d-micro-OCT per tissue type could exceed 10,000. be. Given that the minimum abundance class is 5%, a minimum of 500 positive cells may need to be trained, refined and validated. Assuming a 90% sensitivity and characteristic and a 1:1:1 distribution of samples for training, refinement and validation, ±5% for sensitivity and ±1% for specificity. can achieve a 95% confidence interval of

Additional Technique - Image Registration. Additional techniques can be implemented to improve the registration of the dataset, including providing 3D warping boundary conditions using 3D OCT or microCT imaging of high-precision tape sections and PPFE blocks. include. The frequency components can vary depending on the state of cells. Since d-micro OCT provides a measure of cellular activity, there can be multiple different d-micro OCT periodograms corresponding to the same cell type in IHC.

Rare cell types lead to insufficient sample numbers. If the number of samples is insufficient to adequately analyze rare cell types, selecting alternative tissues (e.g. tonsils versus lymphoid cells), increasing the number of samples and/or e.g. It can be enriched by using other techniques such as cross-validation.

Virtual HE. Here, VHE generated by autofluorescence is used to ensure that all slides and images are coplanar so as not to interfere with antigen retrieval. If VHE is not suitable for pathological diagnosis, other fluorescent agents (such as DAPI and eosin) can be used to generate VHE.

Use of d-micro OCT to Discriminate Live/Apoptotic/Dead Cells in Spheroids

A technique that can rapidly detect live/apoptotic/dead cells in 3D culture models without changing the specimen is expected to improve efficiency in this growing area of biomedical research. Preliminary data (FIG. 11) and other coherence-based motility studies suggest that d-micro-OCT can be used to assess cell viability in samples such as spheroids. Here we validated the ability of d-micro-OCT to distinguish between live/apoptotic/dead cells in three-dimensional culture by imaging treated spheroids from multiple different human and murine melanoma cell lines. can. The d-micro-OCT images can be compared to the gold standard fluorescence-based analysis of cell death performed using Hoechst 33342, annexin V-FITC, and propidium iodide (PI) staining. The results allow us to confirm the accuracy of d-microOCT for this assay and may be a viable alternative for assessing cell viability in 3D culture models.

type and number of specimens; A cell type that is representative of the cells used in 3D culture studies can be used to ensure that results are consistent for a variety of biological and pharmacological conditions. Initial studies can use murine melanoma cells (eg B16.F10). Subsequent tests can be performed using human differentiated melanoma cells (eg A375, etc.) and dedifferentiated melanoma cells (eg RPMI7951).

Details of spheroid growth and generation of treated spheroids are well documented in the literature. Briefly, tumor cells (1×10 ⁶ ) can be seeded in 10 cm ultra-low attachment (ULA) plates to promote spheroid formation. After 24-48 hours, the S2 (40-100 μm) spheroid fraction was pelleted and type I at a concentration of 2.5 mg/mL after addition of 10×PBS containing phenol red pH adjusted with NaOH. It can be resuspended in rat tail collagen (Corning). This spheroid-collagen mixture can then be injected into the central gel region of a 3D microfluidic culture device (DAX-1 Chip, AIM Biotech). After 30 min at 37° C., media (FBS/DMEM 10% and penicillin streptomycin 1%) containing drugs that induce cell death (eg staurosporine STS 1-5 μM) or stimulator controls (DMSO 0.1%). ) allows collagen hydrogels containing tumor spheroids to be hydrated for 24-48 hours.

d-micro OCT imaging. The entire culture chip can be three-dimensionally imaged with d-micro OCT, and the spatial position of each spheroid can be recorded for subsequent spheroid-by-spheroid comparison with confocal or two-photon microscopy.

Assay: After d-micro-OCT imaging, spheroids can be processed for on-chip viability apoptosis staining using Hoechst/PI+/Annexin V-FITC staining. As noted above, dual labeling can be performed by loading the microfluidic device with Hoechst (Ho) and propidium iodide (PI) from Nexcelom.

After confocal microscopy staining, confocal microscopy can be performed on the spheroids. Viable cells stain with Hoechst 33342, but are negative for surface staining of annexin V and have intact cell membranes, excluding PI. Early apoptotic cells become annexin V positive but exclude PI. Dead cells (late apoptotic or necrotic) are stained with PI with or without annexin V.

analysis. The d-micro OCT images can be one-to-one registered to the confocal or two-photon microscopy images of each cell. A custom CellProfiler Analyst pipeline can be used to train and refine the d-micro OCT periodogram images, using the confocally determined cell states as ground truth. Concordance of cell state types (live/apoptotic/dead) can be prospectively specified and quantified using Cohen's kappa coefficient.

Other Additional Approaches—Diversity of Cell Types Multiple human and murine cell types can be evaluated to confirm the accuracy of d-micro-OCT imaging regardless of growth patterns.

Alternative medicine. Initial investigations can use STS, a widely established pan-protein kinase C (PKC) inhibitor that causes caspase-dependent and caspase-independent cell death. To assess physiologically or clinically more important stimuli, mimic anti-tumor immune responses by treating spheroids with exogenous inflammatory cytokines (e.g., TNFa+/IFNg, etc.) and/or MAP kinase. Targeted small molecules (MAPK pathways) can be mimicked.

Identification of the ability of d-micro OCT to detect tumor responses to anticancer agents in tissues

Rapid identification of whether a patient is responding to systemic therapy is an unmet clinical need in oncology. Although preliminary data (FIGS. 11-13) and other intracellular mobility studies suggest that d-micro-OCT should be able to detect kinetic signatures corresponding to cell apoptosis and cell death in tissues, Its sensitivity and specificity are unknown. To fill this gap, d-micro-OCT imaging can be performed ex vivo on pretreated murine tumors. Measurement results can be compared to IHC markers associated with drug efficacy. The data can define frequency signatures corresponding to drug responses, including apoptosis, necrosis, or growth arrest, and provide the accuracy of d-micro OCT to detect the above phenotypes in treated tissues. can be determined.

A technique for in vivo drug response measurement. To expose a live tumor to a chemotherapeutic drug, an IMD is used to place it directly into the tumor, releasing various drugs in minute amounts and in well-defined concentration gradients into the tumor's confined region. be able to. Drug-delivery microdevices are advantageous because they create a confined region within the tumor with highly controlled drug exposure. Also, this device can measure a large set of drug perturbations per mouse (up to 20 reservoirs for each device). Implantable microdevices can be loaded with reservoirs of chemotherapeutic agents such as doxorubicin, cisplatin, olaparib and topotecan, and are applied directly to tumors, eg 300-400 mm ³ in size. Intratumoral delivery can be performed at four topical doses corresponding to IC50, (1/10) IC50, (1/3) IC50, and 3×IC50. Previous measurements with this system showed that apoptosis induction was greatest between 6 and 48 hours of tissue exposure. In order to capture different stages of therapeutic activity and to account for the possible different rates of apoptosis induction within the set of drugs, the drug-delivery IMD was left implanted in the tumor at three time points. For example, the three time points can be 6 hours, 24 hours and 48 hours. For each time point, replicate measurements can be performed for each of the two tumor models. The above four drugs can be provided in every microdevice at the above four concentrations (each microdevice can test these 16 conditions of loaded reservoirs, plus There are also 4 control/stimulator reservoirs). A total of 48 tumors (48 mice) may be required to use n=8 replicate samples at 3 time points in 2 tumor models. At the time of tissue harvest, tumors can be excised from animals and immediately sent for d-micro OCT imaging. Unused portions of the tumor can be saved.

d-Micro-OCT imaging and histological processing. d-Micro-OCT imaging and marking can be performed on resected tumors as described above. After d-micro-OCT, tumor samples can be FFPE-treated and carefully sectioned to ensure cell-level registration. Slides were subjected to multiplex IHC staining using established markers to detect apoptosis and cell death (such as cleaved caspase 3, cleaved caspase 8, cleaved PARP, ph-g-H2AX, and ph-S6). can apply. VHE can be generated.

analysis. The anti-tumor efficacy of each drug can be scored based on the percentage of positively stained cells for each IHC marker (e.g., apoptotic index in a given ROI = [cleaved caspase-3 + number of cells]/ [total number of cells], etc.). Multiple markers associated with cell death can be used because cleaved caspase-3 appears only transiently during the cell apoptotic process. A d-micro OCT periodogram image can be calculated as described above. The d-micro OCT periodogram can be input into a software algorithm such as the CellProfiler machine learning pipeline, and classification rules can be generated by software such as the CellProfiler Analyst using the IHC index of apoptosis or necrosis as ground truth. can be done.

Statistical basis for animal numbers. Based on the variability observed from tumor responses to the drugs used in the above studies and the difference in sensitivity of the two cell lines in vitro, using n=8 replicates per measurement , a statistically significant difference in drug response measured by ground truth IHC with values of p<0.05 and α=0.05 can be obtained. Here, assuming a diagnostic sensitivity/specificity of 90% for d-microOCT pooled across all time points, with 24 animals per tumor model, 95% with a confidence interval of ±12%. can get.

Gender and other important biological variables. Since it is an ovarian tumor, female mice can be used.

Other Additional Approaches--Alternative Tumor Models To broaden the range of cancer types, this study explores SKOV-3 and OVCAR-3 as well as commonly used chemotherapies such as A375, BT474 and PC3. with other human tumor models that respond differently to

Determining how the d-micro OCT signal varies with cell activity, proliferation and metabolism

One characteristic that indicates cancer cells is their rapid rate of proliferation compared to host tissue. In addition, the growth rate of malignant cells has been shown to be a predictor of tumor aggressiveness and viability, and a limited predictor of drug response to cytotoxic chemotherapy. One hypothesis, supported by previous coherence-based motility studies, is that proliferation achieved by increased metabolism, organelle trafficking, and cell endoskeletal reorganization can manifest in the d-micro-OCT signal. . To substantiate this hypothesis, we use well-characterized pharmacological inhibitors of distinct molecular processes associated with proliferation (Table 3) to address the contribution of each cellular process to the d-micro-OCT signature. be able to. This is of great translational importance, and if d-micro-OCT can identify proliferative activity in tumors, it will allow less invasive and earlier assessment of treatment efficacy than is currently feasible. Become.

表３．ターゲット薬物処置の概要 Tumor cells are highly proliferative and motile, with significantly increased metabolism, making them an ideal model system for this study. The human SKOV-3 ovarian cancer tumor model is one example of a suitable system. This model reliably produces predominantly non-necrotic solid tumors in mice over a period of 3 weeks. It is highly proliferative with a cell doubling time of approximately 24-36 hours and exhibits sensitivity to the agents in Table 3. Specimens are taken from the excess tumor tissue generated.

Table 3. Overview of Targeted Drug Treatment

Techniques for tumor growth transformation:
A comprehensive view of changes in the d-micro OCT signal due to drug activity was obtained by longitudinal imaging with an established baseline signal and monitoring at multiple time points over a 24-hour period after drug perturbation. can provide a good evaluation. Thus, tissue slice models of SKOV-3 tumors can be used. Briefly, freshly excised tumors can be sectioned with a tumor matrix or vibratome at 500 μm thickness in culture medium. This section can then be transferred to an organotypic support on a plate. It can hold multiple sections, is capable of cross-sectional imaging, and is filled with culture medium. The plate with the slices can then be maintained in a controlled culture chamber at 37° C. and 5% CO ₂ in a humidified atmosphere for the duration of imaging. For each drug listed in Table 3, a given tumor section can be drug treated at three concentrations: IC50, (1/3) IC50 and 3×IC50. Other additional wells can contain tumor sections treated with vehicle only. n=8 tissue slices for each drug/concentration can be treated in separate wells under identical conditions.

d-Micro OCT Imaging and Histological Imaging: d-Micro OCT imaging can be performed as described above. A baseline image can be obtained immediately prior to addition of the pharmacological compound, and baseline images can be obtained at 30 minutes, 1 hour, 2 hours, 6 hours, 12 hours, and 24 hours after addition. After imaging, the tissue can be FFPE treated and sectioned to create 3D serial HE slides. Other additional IHC stains can also be used to confirm specific pathways and growth inhibitory effects (eg ki67, etc.).

analysis. To account for possible sample degradation, d-micro OCT data obtained from drug-treated wells can be normalized against data obtained from vehicle-treated specimens. A d-micro OCT periodogram image can then be computed as described above. Changes in the d-micro-OCT periodogram can be assessed pixel-by-pixel, cell-by-cell, and tissue-by-tissue, depending on drug, concentration and time point.

Additional Approach--Tissue Degradation If significant tissue degradation is observed in untreated vehicle samples, the growth medium can be changed periodically between d-micro-OCT measurements. Additionally, tissue slice thickness can be reduced to improve nutrient diffusion in the center of the specimen. Also, available media perfusion systems can be incorporated into the controlled culture chambers described above.

Development of novel d-micro OCT techniques for improved characterization of intracellular motility: increasing spatial and frequency resolution of d-micro OCT

表４．ｄ－マイクロＯＣＴ系の分解能パラメータ。 Optical spatial resolution. Table 4 shows the spatial resolution performance parameters of the current d-micro OCT system and the new d-micro OCT system. Optical spatial resolution can be improved by one or more of the following: 1) the center wavelength of the micro-OCT light source is reduced from 800nm to 700nm, 2) the bandwidth of the micro-OCT light source is increased from 300nm to 400nm. 3) the numerical aperture (NA) of the sample arm lens is increased from NA=0.12 to NA=0.2; 4) the DOF is extended over 20x while maintaining cross-sectional imaging in the 300 μm range. A known extended depth of focus (EDOF) technique is implemented. Although attempts to exceed this spatial resolution performance are possible, the parameters shown in Table 4 were conservatively chosen to double axial and lateral resolution without significantly compromising transverse imaging performance or depth of penetration. The spatial resolution of the novel d-micro OCT system can be verified by knife-edge scans, z-scans, and imaging of resected tissue at standard resolution. Success can be defined when two improved resolution factors in the x, y and/or z dimensions are seen.

Table 4. Resolution parameters of the d-micro OCT system.

Computational spatial super-resolution. Using d-micro-OCT, multiple images or A-lines are acquired from the same portion of the sample in the presence of both intracellular motion (signal) and large-scale bulk motion. Furthermore, the micro-OCT EDOF technique that focuses the annular pattern onto the sample aliases high spatial frequency information onto the lens. Bulk motion and aliasing can computationally increase the spatial resolution without changing the device configuration or acquisition process. FIG. 16 shows a standard d-micro-OCT image and a spatial super-resolution d-micro-OCT image generated from the same micro-OCT dataset. Super-resolution images were computed by performing Gauss-Newton nonlinear least-squares minimization with a hybrid bidiagonalization regularization process and a full-image affine transformation geometric warping process. Here, the accuracy and performance of the above algorithms can be improved by incorporating multi-scale local registration and elastic unwarping. Combined with 3D beam scanning, out-of-plane data can be recovered to reconstruct super-resolution images in three dimensions. The above results can be verified by comparing the super-resolution images of the phantom and tissue with the corresponding histological images using the same technique as above. Success can be defined if a two-fold increase in resolution is seen.

Computational frequency super-resolution. Increased frequency resolution can significantly improve the discriminative power of d-micro-OCT, especially at low frequencies (0-10 Hz) where most of the intracellular movements occur. Since the total measurement duration determines the frequency resolution, it can be found whether additional frequency information content is obtained by increasing the acquisition time. However, extended acquisition times are undesirable in many scenarios and become intolerable for in vivo image acquisition. Therefore, high-resolution DFT (HRDFT) and extended DFT (EDFT) algorithms, which can increase the power spectral frequency resolution in discrete bandwidths without changing the total acquisition time, were used to obtain d-micro-OCT periodograms. You can study computing. Frequency resolution can be tested using magnetically modulated microbeads embedded in phantoms, cells, and tissues. Success can be defined when a two-fold increase in frequency resolution from 0-10 Hz is observed.

Development of novel algorithms to process and extract more information from d-micro OCT signals

Time-frequency analysis. Short-time Fourier transform (STFT) analysis of (TFA) d-micro-OCT tissue motility signals suggests that the intracellular movements are non-stationary. Thus, the use of TFA may increase the information extractable from d-micro-OCT. The Stockwell transform (ST) can be used, but due to the super-resolution ability of the Stockwell transform at low frequencies, other techniques including STFT, wavelets, Wigner-Ville, constant-Q Gabor transforms, and other time-frequency representations can be used. Possible procedures can be used to improve discrimination. TFA can be applied to the signal after 3D elastic unwarping. Feature extraction can be performed using an energy concentration hybrid classification scheme and time-frequency cluster analysis. A d-micro OCT image of a magnetomotive phantom excited with a predetermined non-stationary waveform can be obtained. TFAs can be compared to known frequency stimuli and are defined as successful if there is less than 10% variability.

machine learning. To dig deeper into the d-micro-OCT data, a deep neural network (NN) is trained to jointly model morphological and functional features, grouping individual pixels into biologically relevant categories. You can learn spatio-temporal expressions that can be used. The input can be the d-micro-OCT periodogram and the matched histological image dataset acquired as described above, and the output can be a sequence of two-dimensional probabilistic segmentation images. , each of which corresponds to a biological category of interest. Recurrent convolutional NNs and fully convolutional NNs that can jointly model dynamic patterns in a spatial organization context can be used. To train the model, we can apply both supervised and semi-supervised learning approaches that use graphic image annotation strategies to obtain pixel-level ground truth labels. Based on the training set, multiple models can be trained on a wide variety of network architectures and hyperparameters to select the final NN model. Then select the model that performs best on the holdout validation set to identify the optimal model depth, width and resolution. To evaluate this selected model and estimate the generalization error, using a separate test data set, quantitative measurements derived from the model-predicted semantic segmentation for a given image; Predicted histological patterns for known cell/tissue types and pathological conditions can be compared. Furthermore, the resulting NN-generated single-cell measurement results were analyzed using multivariable statistical methods and unsupervised machine learning, and the cell phenotype, frequency components, life-and-death state, and activation state were analyzed. Cells can be clustered. Lower-dimensional representations of the above high-dimensional datasets (eg, t-SNE, UMAP, etc.) can be created to facilitate visualization and discovery of subpopulations of interest.

Development and testing of methods for performing d-micro-OCT in vivo Elastic unwarping of multiplanar beam scanning two dimensions can correct for in-plane bulk motion, but data moved out of the imaging plane cannot be captured. To alleviate this problem, a novel beam scanning technique can be developed that acquires d-micro-OCT images in multiple adjacent transverse planes, performing registration and elastic unwarping processes in three dimensions. be able to. Our first approach to multi-plane scanning can be raster dithering of the beam (Fig. 8, scan mode 3). When it becomes necessary to retain higher frequency information, d-micro OCT imaging of coherence multiplexed or photoswitched images from multiple spatially offset out-of-plane sample beams (Fig. 8, scan mode 4) can be implemented. Since these beam scanning techniques acquire 3D information, this data can be used to determine motion vectors in a manner similar to diffusion tensor MRI. The d-micro-OCT results obtained from static images after 3D unwarping processing of resected tissue with or without deterministic sample out-of-plane motion were calculated as mean squared error, structural similarity index (SSIM), Or they can be compared using an image comparison metric (ICM) such as Mander's overlap coefficient. Success can be defined as less than 10% variation in frequency content for in-plane and out-of-plane motion.

Handheld d-micro OCT probe with stabilization. It has been possible to acquire mechanically stabilized d-micro OCT images of human skin in vivo using a benchtop microscope (Fig. 15). To expand the in vivo use of d-micro OCT, we introduce a 15 mm diameter handheld d-micro OCT probe with built-in stabilization technology. This is applicable to dermatological and surgical imaging external surfaces. The probe can be specially configured to overcome problems associated with long acquisition times and sub-micrometer accuracy. Beam scanning can be performed using MEMS and/or microgalvanometer technology capable of producing repeatable beam scanning patterns, including standard imaging (Fig. 8, scan mode 1), high frequency imaging (Fig. 8, scan mode 2), and multiplanar imaging (FIG. 8, scan modes 3 and 4). The probe incorporates mechanical and aspiration stabilization techniques, implements active image feedback and beam scanner control, and patterns gated to physiological conditions such as cardiac, respiratory and/or peristaltic cycles. can be scanned.

After developing the handheld d-micro OCT probe described above, the feasibility of using the technique to acquire skin images in vivo can be tested in a clinical pilot study. In this clinical pilot study, patients (eg, n=10: 5 males/5 females) undergo elective lesion resection. 3D imaging of lesions can be performed in vivo prior to resection. After resection, the specimen can be placed in media and 3D imaged again ex vivo using a benchtop d-micro OCT system. The resection can then be sent to pathology according to standard of care. d-Micro OCT images of human skin obtained in vivo can be compared with images obtained ex vivo using frequency-dependent ICM. Assuming a correlation of 0.9 between the in vivo image and the corresponding ex vivo image, statistical significance can be obtained with a power of 0.8 and α=0.05.

Another embodiment of the present disclosure is a dual-beam phase-sensitive d-micro OCT (DBPS-d-micro OCT) system, which can be used as a handheld or endoscopic probe for in-vivo or ex-vivo imaging. can be done. It is difficult to obtain motility information in a single scan, because it is difficult to control the beam with micrometer-level precision in the presence of motion, and continuous imaging of the same location is difficult. Important for the imaging of living internal organs with endoscopic probes, intracellular movements are assessed in a single scan by measuring the phase change of light reflected from the same location within the sample over a given period of time. There is a possibility that it can be done (Fig. 8, scan mode 5). In practice, two spatially offset micro-OCT imaging beams (FIG. 17) can be used to acquire two phase-resolved micro-OCT images of the same site. These two micro-OCT images are separated by Δt=d/v, where v is the speed of the scan and d is the spatial offset of the two beams. FIG. 17 is a schematic illustration of a dual-beam phase-sensing d-micro OCT (DBPS-d-micro OCT) system attached to a flexible probe configured for deployment within an accessory port of an endoscope; be. Two micro-OCT fiber-based optical assemblies (SMF, MMF, GRIN, prism) that focus the beam to a 2 μm diameter with 20-30× DOF expansion can be mechanically coupled to the linearly moving drive shaft, The drive shaft can be contained within the 2 mm diameter outer sheath of the probe. The beam of the optical assembly may exit through a transparent window, and the focal points of the optical assembly may be separated from each other by d at the sample. A fiber-based Michelson interferometer with a high-speed fiber optic switch in the sample arm can enable dual-beam micro-OCT imaging in one spectroscopic detection unit. Each individual micro-OCT image produced by the beams of each optical assembly may be images from alternating A-lines, e.g., images produced by assembly 1 may be images from odd A-lines and assembly 2 images can be created from an even number of A-lines. This configuration can eliminate phase drift by using only one detection unit while halving the imaging speed, and can greatly improve phase stability. A d-micro OCT image can be computed as a phase-difference image (x, y, Δt) and nanometer-level displacements can be detected. As typical values, for a scan range of 2 mm, d=1 mm, an A-line rate of 20 kHz, and an A-scan of 1 per micrometer, sampling can yield d-micro-OCT images of length 1 mm at Δt=25 ms. . Assuming a sensitivity of 90 dB and an estimated phase stability of −3 mrad (a stationary mirror is sampled), DBPS-d-microOCT should be able to detect displacements on the order of −2 nm (which is equivalent to lateral for the scan—corresponding to a phase stability of 30 mrad), which allows one image to provide the same frequency resolution as 1000 images acquired using conventional techniques. This three orders of magnitude increase in frequency resolution is made possible by the superior phase sensitivity of SDOCT. It should be noted that this frequency resolution improvement can be exploited for ex vivo samples. Verification can be performed by comparing DBPS-d-micro OCT of resected upper gastrointestinal tissue with conventional (scan mode 1) d-micro OCT of the same upper gastrointestinal tissue using frequency dependent ICM. Success can be defined as a frequency/sample match variation of less than 10%.

The DBPS probe could potentially be used to obtain endoscopic d-micro-OCT in-vivo images of patients undergoing upper endoscopy. Normal areas and abnormalities (e.g., Barrett's mucosa) observed by video endoscopy were observed by the clinician with a DBPS probe inserted into the endoscope's accessory channel, similar to previous studies conducted by the present inventors. and can be imaged in vivo by d-micro-OCT.

Computer System and Processing Referring to FIG. 18, an example system 1800 (eg, data acquisition and processing system, etc.) for obtaining image and functional data from living tissue is shown in accordance with some embodiments of the presently disclosed subject matter. In some embodiments, computer 1810 can execute at least a portion of system 1804 for acquiring image and functional data from biological tissue and provide control signals to interferometer 1802 . Additionally or alternatively, in some embodiments, computing device 1810 can exchange information regarding the above control signals with server 1820 via communication network 1806, which communicates information from living tissue. At least a portion of system 1804 for acquiring image data and functional data can be implemented. In some embodiments described above, server 1820 may return information to computer 1810 (and/or other suitable computers) regarding control signals for obtaining image data and functional data from anatomy 1804 . This information can be transferred and/or presented to a user (e.g., researcher, operator, clinician, etc.) and/or stored (e.g., as part of a research database or chart associated with the subject). be able to.

In some embodiments, computer 1810 and/or server 1820 may be any suitable computer or combination of computers, such as desktop computers, laptop computers, smart phones, tablet computers, wearable computers, server computers. , a virtual machine run by a physical computer, or the like. As described above, the system 1804 for acquiring image data and functional data from biological tissue can present information regarding control signals to a user (eg, researcher and/or physician). In some embodiments, interferometer 1802 can comprise a device such as the device shown in FIG.

In some embodiments, communication network 1806 may be any suitable single communication network or any suitable combination of communication networks. For example, communication network 1806 may be a WiFi network (which may include one or more wireless routers, one or more switches, etc.), a peer-to-peer network (eg, Bluetooth network, etc.), a cellular network (eg, 3G networks, 4G networks, etc., compliant with any suitable standard such as CDMA, GSM, LTE, LTE Advanced, WiMAX, etc.), wired networks, and the like. In some embodiments, communication network 1806 is a local area network, a wide area network, a public network (such as the Internet), a private or semi-private network (such as a corporate or university intranet), any other suitable type of network; or any suitable combination of networks. Each of the communication links shown in FIG. 18 may be any suitable one or any suitable combination of communication links, such as a wired link, fiber optic link, WiFi link, Bluetooth link, cellular link, or the like.

FIG. 19 is a diagram illustrating example hardware 1900 that can be used to implement computer 1810 and server 1820 of some embodiments of the disclosed subject matter. As shown in FIG. 19, in some embodiments, calculator 1810 includes processor 1902, display 1904, one or more inputs 1906, one or more communication systems 1908, and/or memory 1910. can be done. In some embodiments, hardware processor 1902 may be any suitable hardware processor or combination of hardware processors, such as a central processing unit, graphics processing unit, and the like. In some embodiments, display 1904 may comprise any suitable display device, such as a computer monitor, touch screen, television, or the like. In some embodiments, input 1906 may comprise any suitable input device and/or sensors that can be used to receive user input, such as a keyboard, mouse, touch screen, microphone, and/or the like.

In some embodiments, communication system 1908 includes any suitable hardware, firmware and/or software for communicating information over communication network 1806 and/or any other suitable communication network. be able to. For example, communication system 1908 can include one or more transceivers, one or more communication chips, and/or one or more communication chipset, and/or the like. In one more specific example, communication system 1908 can include hardware, firmware and/or software that can be used to establish WiFi connections, Bluetooth® connections, cellular connections, Ethernet connections, and the like. can.

In some embodiments, memory 1910 may include any suitable storage device(s) that may be used to store instructions, values, etc., which may be displayed by processor 1902, for example. It can be used to present content using the portion 1904, to communicate with the server 1820 via the communication system(s) 1908, and so on. Memory 1910 may include any suitable volatile memory, non-volatile memory, storage, any other suitable type of storage medium, or any suitable combination thereof. For example, memory 1910 can include RAM, ROM, EEPROM, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and the like. In some embodiments, memory 1910 may contain encoded computer programs for controlling the operation of computer 1810 . In such embodiments, processor 1902 executes at least a portion of a computer program to present content (eg, images, user interfaces, graphics, tables, etc.), receive content from server 1820, transfer information to server 1820, etc. can be executed.

In some embodiments, server 1820 may comprise processor 1912 , display 1914 , one or more inputs 1916 , one or more communication systems 1918 , and/or memory 1920 . In some embodiments, hardware processor 1912 may be any suitable hardware processor or combination of hardware processors, such as a central processing unit, graphics processing unit, and the like. In some embodiments, display 1914 may comprise any suitable display device, such as a computer monitor, touch screen, television, or the like. In some embodiments, input 1916 may comprise any suitable input device and/or sensors that can be used to receive user input, such as a keyboard, mouse, touch screen, microphone, and/or the like.

In some embodiments, communication system 1918 includes any suitable hardware, firmware and/or software for communicating information over communication network 1806 and/or any other suitable communication network. be able to. For example, communication system 1918 can include one or more transceivers, one or more communication chips, and/or one or more communication chipset, and/or the like. In one more specific example, communication system 1918 can include hardware, firmware and/or software that can be used to establish WiFi connections, Bluetooth® connections, cellular connections, Ethernet connections, and the like. can.

In some embodiments, memory 1920 can include any suitable storage device(s) that can be used to store instructions, values, etc., which can be displayed by processor 1912, for example. It can be used to present content using unit 1914, to communicate with one or more computing devices 1810, and the like. Memory 1920 may include any suitable volatile memory, non-volatile memory, storage, any other suitable type of storage medium, or any suitable combination thereof. For example, memory 1920 can include RAM, ROM, EEPROM, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and the like. In some embodiments, memory 1920 may contain encoded server programs for controlling the operation of server 1820 . In such embodiments, processor 1912 processes information from one or more computers 1810 to transfer information and/or content (eg, tissue identification and/or classification results, user interface, etc.) to one or more computers 1810 . and/or execute at least a portion of such server programs to receive content, such as to receive commands from one or more devices (e.g., personal computers, laptop computers, tablet computers, smart phones, etc.); be able to.

In some embodiments, any suitable computer-readable medium may be used for storing instructions for performing the functions and/or processes described herein. For example, in some embodiments computer readable media may be transitory or non-transitory. For example, non-transitory computer-readable media include magnetic media (e.g., hard disks, floppy disks, etc.), optical discs (e.g., compact discs, digital video discs, Blu-ray (registered trademark) discs, etc.), semiconductor media (e.g., RAM , flash memory, electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)), any suitable medium that does not lose or lack any form of permanence during transfer; and/or may include a medium such as any suitable tangible medium. As another example, a transitory computer-readable medium may be a wire, conductor, fiber optic, circuit, or any suitable medium that does not drop or lack any form of permanence in transit over a network; and/or may include a signal on any suitable intangible medium.

It should be noted that the term "mechanism" as used herein may include hardware, software, firmware, or any suitable combination thereof.

FIG. 20 illustrates an example process 2000 for obtaining image and functional data from living tissue of some embodiments of the disclosed subject matter. As shown in FIG. 20, process 2000 can acquire interference information at 2002 based on radiation provided by interference of living tissue with a reference along an imaging plane at multiple times. Interference information can be obtained using an interferometer. At 2004, the process 2000 can process the interference information to generate a morphological image of the anatomy along the imaging plane. This processing can be performed using a processor configured to receive interference information from the interferometer. At 2006, process 2000 can determine frequency information based on the multiple points in time of the interference information. This frequency information can be determined using a processor. Frequency information can reflect temporal modulation caused by the mechanical function of living tissue. At 2008, the process 2000 can determine a spatial map of the frequency information and the morphological image. This spatial map can be determined using a processor. A spatial map of frequency information can indicate the mechanical function of living tissue. Finally, process 2000 can generate a report at 2010 based on the identification of the spatial map. A report can be generated using the processor.

It should be understood that the above steps of the process of FIG. 20 can be performed or performed in any order or sequence, without being limited to the order or sequence shown or described. Also, some of the above steps of the process of FIG. 20 may be performed or performed substantially concurrently, where appropriate, or may be performed or performed in parallel to reduce latency or processing time.

In certain embodiments, the steps of any process disclosed herein can be performed using a processor in communication with a memory containing instructions that cause the processor to perform the process. In some embodiments, memory may include any suitable computer-readable medium usable for storing instructions for performing the functions and/or processes described herein. For example, in some embodiments computer readable media may be transitory or non-transitory.

Although the subject matter of the present disclosure has been described above with reference to specific embodiments and examples, those skilled in the art will appreciate that the invention need not be so limited and that numerous other embodiments, examples, uses, It is clear that modifications and derivatives from the above embodiments, examples and uses are also intended to be covered by the appended claims. The entire disclosure of each patent and publication cited herein is incorporated by reference into this disclosure, as if each such patent or publication were individually incorporated by reference into this disclosure.

Various features and advantages of the invention are set forth in the appended claims.

Claims (58)

An apparatus for obtaining image data and functional data from a biological sample, comprising:
An interferometer configured to acquire interference information based on radiation provided by interference of the biological sample with a reference at a plurality of time points along an imaging plane, wherein at least one axis of the imaging plane. is an interferometer whose axis is at least partially along the depth dimension or the axial dimension;
a processor configured to receive the interference information from the interferometer;
and
The processor
processing the interference information to generate an image of the biological sample along the imaging plane;
determining frequency information reflecting temporal modulation induced by a mechanical function of the biological sample based on the plurality of time points of the interference information;
A device configured to generate a spatial map of said frequency information indicative of said mechanical function of said biological sample. When obtaining the frequency information, the processor further:
configured to identify temporal variations in the interferometric information caused by the mechanical function of the biological sample;
Apparatus according to claim 1. When identifying the temporal variation, the processor further includes:
configured to perform a power frequency analysis of the temporal variations to identify variations caused by intracellular motion of the biological sample;
3. Apparatus according to claim 2. the image has a plurality of sub-regions,
The processor is further configured to individually perform the power frequency analysis for at least one of the plurality of sub-regions.
4. Apparatus according to claim 3. the interferometer is further configured to acquire the interference information at multiple time points during a longitudinal study of at least 24 hours;
The processor is further configured to track mechanical function of the biological sample during the longitudinal study.
5. Apparatus according to claim 4. applying a drug to the biological sample during the longitudinal study;
6. Apparatus according to claim 5. said drug is a chemotherapeutic drug;
7. Apparatus according to claim 6. The processor is further configured to determine the entropy of the frequency spectrum to quantify the frequency content of the signal.
3. Apparatus according to claim 2. the interferometer is part of a micro-OCT system;
Apparatus according to claim 1. wherein the micro-OCT system provides a lateral resolution of at least 2 μm or an axial resolution of at least 1 μm of the interferometric information;
10. Apparatus according to claim 9. said biological sample is an in vivo sample;
Apparatus according to claim 1. the interference information is a micro OCT frame;
The processor, when processing the interference information to generate an image of the biological sample along the imaging plane, further comprising:
subjecting the micro-OCT frame to local normalization and Gaussian filtering to produce a processed frame;
calculating an elastic unwarping transformation matrix for each of said processed frames with reference to the center frame;
configured to apply the transform matrix to the micro-OCT frames;
Device according to any one of claims 1 to 11. further comprising a galvanometer configured to scan at the scan rate,
When acquiring the interference information, the interferometer further:
configured to acquire the interference information at a frequency determined by a scan rate of the galvanometer by repeatedly scanning an imaging beam laterally in a region of interest;
Apparatus according to claim 1. When acquiring the interference information, the interferometer further:
stepping an imaging beam across a lateral region of interest in multiple scans;
configured to acquire a series of A-lines at a rate determined by the A-line rate by stopping at equally spaced positions in the lateral region of interest during each of the plurality of scans;
Apparatus according to claim 1. The processor further:
obtaining a cross-correlation between at least two image frames to measure the amount of lateral shift;
configured to apply an image registration algorithm to the at least two image frames to correct for the lateral shift;
Apparatus according to claim 1. said interferometer being part of a micro-OCT system;
The processor, when processing the interference information to generate an image of the biological sample, further comprising:
configured to correct for depth-dependent attenuation of micro-OCT intensity in the axial direction;
Apparatus according to claim 1. When obtaining the frequency information, the processor further:
configured to determine the frequency information for a subset of the plurality of time points;
Apparatus according to claim 1. When determining the frequency information based on the plurality of time points, the processor further:
configured to determine the frequency information by performing spectral analysis for the plurality of time points based on at least one of power spectrum, standard deviation, variance, or Fourier entropy;
Apparatus according to claim 1. The processor, when generating the image of the biological sample, further:
configured to generate a super-resolution image of the biological sample;
Apparatus according to claim 1. The processor, when generating the super-resolution image of the biological sample, further:
configured to generate the super-resolution image of the biological sample based on performing Gauss-Newton nonlinear least-squares minimization with a hybrid bidiagonalization regularization process and a full-image affine transform geometric warping process;
20. Apparatus according to claim 19. wherein the biological sample comprises biological tissue;
Apparatus according to claim 1. the biological sample comprises a two-dimensional culture or a three-dimensional culture;
Apparatus according to claim 1. said biological sample comprises at least one of a spheroid, an organoid, or an organ chip;
23. Apparatus according to claim 22. wherein the interferometer is configured to obtain the interferometric information using an extended depth of focus (EDOF) technique;
Apparatus according to claim 1. the interferometer is further configured to obtain the interference information including at least one of phase information or intensity information;
The processor, when processing the interference information to generate an image of the biological sample, further comprising:
processing the interference information to generate the image of the biological sample based on at least one of the phase information or the intensity information;
Apparatus according to claim 1. When obtaining the frequency information, the processor further:
configured to determine the frequency information based on providing a real-time estimate of the frequency information;
Apparatus according to claim 1. The processor, in providing a real-time estimate of the frequency information, further comprising:
configured to provide a real-time estimate of said frequency information based on moving standard deviation or moving variance;
27. Apparatus according to claim 26. the interferometer is further configured to acquire the interference information at multiple points in time using a two-dimensional scan pattern;
The processor, when processing the interference information to generate an image of the biological sample, further comprising:
configured to process the interference information to generate a three-dimensional image of the biological sample;
Apparatus according to claim 1. the interferometer is further configured to acquire the interference information at multiple points in time using a raster scan pattern;
The processor, when processing the interference information to generate an image of the biological sample, further comprising:
configured to process the interference information to generate a three-dimensional image of the biological sample;
Apparatus according to claim 1. The processor, in generating the spatial map of the frequency information, further comprising:
configured to generate a report based on identifying the spatial map;
Apparatus according to claim 1. A method for obtaining image data and functional data from a biological sample, comprising:
obtaining, with an interferometer, interference information based on radiation provided by interference of the biological sample with a reference along an imaging plane at multiple times;
provided that at least one axis of the imaging plane is an axis along at least a depth dimension or an axial dimension;
processing the interference information to generate an image of the biological sample along the imaging plane with a processor configured to receive the interference information from the interferometer;
determining, with the processor, frequency information reflecting temporal modulation induced by mechanical features of the biological sample based on the plurality of time points of the interference information;
generating, with the processor, a spatial map of the frequency information for the image, the spatial map indicating the mechanical function of the biological sample;
A method comprising: Determining the frequency information further comprises:
identifying temporal variations in the interferometric information caused by the mechanical function of the biological sample;
32. The method of claim 31. Identifying the temporal variation further comprises:
performing a power frequency analysis of the temporal variations to identify variations caused by intracellular motion of the biological sample;
33. The method of claim 32. the image has a plurality of sub-regions,
The method further includes separately performing the power frequency analysis for at least one of the plurality of sub-regions.
34. The method of claim 33. The interferometer further comprises:
obtaining the interference information at multiple time points during a longitudinal study of at least 24 hours using the interferometer;
configured to track, with the processor, the mechanical function of the biological sample during the longitudinal study;
35. The method of claim 34. further comprising applying a drug to the biological sample during the longitudinal study;
36. The method of claim 35. said drug is a chemotherapeutic drug;
37. The method of claim 36. determining the entropy of the frequency spectrum to quantify the frequency content of the signal;
33. The method of claim 32. the interferometer is part of a micro-OCT system;
32. The method of claim 31. When acquiring the interference information, the interferometer further:
using the micro-OCT system to achieve a lateral resolution of at least 2 μm or an axial resolution of at least 1 μm;
40. The method of claim 39. wherein the biological sample comprises in vivo tissue;
32. The method of claim 31. the interference information is a micro OCT frame;
Processing the interference information to generate an image of the biological sample along the imaging plane further comprises:
subjecting the micro-OCT frame to local normalization and Gaussian filtering to generate a processed frame;
calculating an elastic unwarping transformation matrix for each processed frame with reference to a center frame;
applying the transform matrix to the micro-OCT frames;
including,
42. The method of any one of claims 31-41. Obtaining the interference information further includes:
configured to use the interferometer to repeatedly scan an imaging beam laterally in a region of interest using a galvanometer to obtain the interference information at a frequency determined by a scan rate of the galvanometer;
32. The method of claim 31. Obtaining the interference information further includes:
stepping an imaging beam across a lateral region of interest with the interferometer in multiple scans;
acquiring a series of A-lines at a rate determined by the A-line rate by stopping at equally spaced positions in the lateral region of interest during each of the plurality of scans with the interferometer;
32. The method of claim 31, comprising: moreover,
cross-correlating between at least two image frames to measure the amount of lateral shift;
applying an image registration algorithm to the at least two image frames to correct for the lateral shift;
including,
32. The method of claim 31. said interferometer being part of a micro-OCT system;
Processing the interference information to generate an image of the biological sample further comprises:
correcting for depth-dependent attenuation of micro-OCT intensity in the axial direction;
32. The method of claim 31. Determining the frequency information further comprises:
determining the frequency information for a subset of the plurality of time points;
32. The method of claim 31. An apparatus for obtaining image data and functional data from a sample, comprising:
An interferometer for obtaining interference information based on radiation provided by interference of said sample with a reference along an imaging plane, wherein at least one axis of said imaging plane is in a depth dimension or an axial dimension. an interferometer that is at least partially along an axis;
a processor configured to receive the interference information from the interferometer;
and
The processor
processing the interference information to generate an image of the sample along the imaging plane;
configured to perform a frequency analysis of temporal variations caused by the samples;
The analysis is
determining spectral information of the interference information;
binning the spectral information into a plurality of frequency ranges;
generating a pseudo-color composite image including a plurality of colors corresponding to each of the plurality of frequency ranges, the pseudo-color composite image including contrast portions corresponding to differences in intracellular motion within the sample;
An apparatus comprising: the temporal variation is caused by at least one of intracellular movement within the sample or a mechanical function of the sample;
49. Apparatus according to claim 48. An apparatus for obtaining image data and functional data from a biological sample, comprising:
An interferometer configured to acquire interference information based on radiation provided by interference of the biological sample with a reference at a plurality of time points along an imaging plane, wherein at least one axis of the imaging plane. is an interferometer whose axis is at least partially along the depth dimension or the axial dimension;
a processor configured to receive the interference information from the interferometer;
and
The processor
processing the interference information to generate an image of the biological sample having a plurality of pixels along the imaging plane;
estimating a frequency spectrum for each of the plurality of pixels by using a time-frequency analysis of the temporal modulation of the interferometric information acquired at the plurality of time points and caused by mechanical features of the biological sample;
A device configured to generate a report spatially mapping the mechanical features of the biological sample to the image. wherein the interferometer is configured to acquire the interferometric information, including phase-resolved interferometric information, at multiple time points using two spatially offset beams of radiation;
51. Apparatus according to claim 50. The processor, when processing the interference information to generate an image of the biological sample, further comprising:
configured to process the interference information to generate a pair of images;
the interference information for each image of the pair is generated by alternating A-lines in the imaging plane;
52. Apparatus according to claim 51. The processor, when processing the interference information to generate an image of the biological sample, further comprising:
configured to process the pair of images to generate a phase contrast image;
53. Apparatus according to claim 52. A method for obtaining image data and functional data from a biological sample, comprising:
Radiation provided by interfering said biological sample with a reference along an imaging plane in which at least one axis is along at least partly the depth dimension or the axial dimension at multiple time points using an interferometer. obtaining interference information based on
processing the interference information to generate an image of the biological sample along the imaging plane with a processor configured to receive the interference information from the interferometer;
determining, with the processor, frequency information reflecting temporal modulation induced by mechanical features of the biological sample based on the multiple time points of the interference information using a dimensionality reduction analysis;
generating, with the processor, a spatial map of the frequency information for the image, the spatial map indicating the mechanical function of the biological sample;
A method comprising: the dimensionality reduction analysis includes principal component analysis (PCA);
Determining the frequency information further comprises:
identifying principal components representing less than all of the variance of the power spectrum of the frequency information;
Generating a spatial frequency map of the frequency information further comprises:
generating a principal component image based on the identified principal components;
The method further comprises:
identifying a frequency pattern based on the principal component image;
55. The method of claim 54. Generating the principal component image further comprises:
generating a multicolor image by mapping the principal component image values to visible colors;
56. The method of claim 55. Identifying principal components representing less than all of the variance of the power spectrum of the frequency information further comprises:
identifying principal components representing most of the variance of the power spectrum of said frequency information;
56. The method of claim 55. the dimensionality reduction analysis comprises at least one of principal component analysis (PCA), kernel PCA, linear discriminant analysis, generalized discriminant analysis, UMAP, or t-SNE;
55. The method of claim 54.

Images